Greenness Assessment of HPTLC-Densitometry Methods: A Comprehensive Guide for Sustainable Pharmaceutical Analysis

Abstract

This article provides a comprehensive overview of the principles, tools, and applications for assessing the environmental impact of High-Performance Thin-Layer Chromatography (HPTLC)-densitometry methods. Tailored for researchers, scientists, and drug development professionals, it explores the foundational concepts of green analytical chemistry, details established and emerging greenness assessment metrics like AGREE, AES, and MoGAPI, and presents methodological applications for sustainable pharmaceutical analysis. The content further addresses common troubleshooting and optimization strategies, outlines rigorous validation protocols integrating green principles, and offers comparative analyses with other chromatographic techniques. By synthesizing current literature and practical case studies, this guide serves as a vital resource for implementing eco-friendly analytical practices in pharmaceutical quality control and development.

Principles and Metrics for Green HPTLC-Densitometry

Green Analytical Chemistry (GAC) represents a transformative approach within pharmaceutical analysis, focusing on the development and application of analytical methods that minimize environmental impact while maintaining high standards of accuracy, precision, and reliability [1] [2]. The core principles of GAC advocate for reducing or eliminating hazardous solvent consumption, decreasing energy requirements, minimizing waste generation, and implementing safer procedures for analysts [1] [3]. This paradigm shift responds to the growing recognition that traditional analytical methods, particularly in quality control laboratories, often involve substantial quantities of toxic solvents and generate significant waste, creating environmental and occupational hazards [4] [5].

The adoption of GAC principles has become increasingly crucial in pharmaceutical analysis, where routine testing of active pharmaceutical ingredients (APIs), formulations, and impurities demands sustainable solutions [3]. High-performance thin-layer chromatography-densitometry (HPTLC-densitometry) has emerged as a frontrunner in this green revolution, offering several inherent environmental advantages over traditional methods like High-Performance Liquid Chromatography (HPLC) [6] [4]. These advantages include significantly reduced solvent consumption, lower energy requirements, elimination of costly analytical columns, and the capability to analyze multiple samples simultaneously on a single plate [6].

GAC Assessment Metrics and Tools

The evaluation of analytical methods' environmental impact requires specialized metrics and assessment tools. Multiple standardized approaches have been developed to quantitatively and qualitatively measure the greenness of analytical procedures [1].

Table 1: Key Green Analytical Chemistry Assessment Tools

Assessment Tool	Type	Key Parameters Measured	Output Format
Analytical Eco-Scale [2]	Semi-quantitative	Penalty points for hazardous reagents, energy consumption, waste	Numerical score (ideal: 100)
NEMI (National Environmental Methods Index) [5]	Qualitative	Persistence, bioaccumulation, toxicity, corrosivity	Pictogram (pass/fail for 4 criteria)
GAPI (Green Analytical Procedure Index) [7] [4]	Qualitative	Multiple aspects from sample collection to waste treatment	Pictogram with 15 colored segments
AGREE (Analytical GREENness) [4] [5]	Quantitative	12 principles of GAC with weighting factors	Score 0-1 with circular pictogram
ChlorTox [8] [1]	Quantitative	Chlorinated solvent toxicity	Mass in grams
BAGI (Blue Applicability Grade Index) [6] [7]	Qualitative	Method practicality and applicability	Numerical score

The Analytical Eco-Scale assigns penalty points to parameters that deviate from ideal green analysis, with a score of 100 representing an ideal green method and scores above 75 indicating excellent greenness [8]. The AGREE metric utilizes a software-based approach that evaluates all 12 principles of GAC, generating a score between 0 (not green) and 1 (ideal green) accompanied by an easily interpretable circular pictogram [1] [4]. The GAPI tool provides a more comprehensive pictogram that covers the entire analytical procedure across five steps with 15 evaluation criteria [7].

Recent research demonstrates the application of these tools in method validation. For instance, a greener reversed-phase HPTLC method for apremilast analysis achieved outstanding scores across multiple metrics: Analytical Eco-Scale score of 93, ChlorTox value of 0.66 g, and AGREE score of 0.89 [8].

HPTLC-Densitometry as a Green Alternative

HPTLC-densitometry offers significant environmental advantages that align with GAC principles, making it a sustainable alternative to traditional chromatographic methods in pharmaceutical analysis [6] [4].

Environmental and Practical Advantages

The green credentials of HPTLC-densitometry stem from several inherent characteristics. Unlike HPLC methods that require continuous solvent flow throughout analysis, HPTLC utilizes minimal mobile phase volumes (approximately 10-20 mL per run) regardless of the number of samples, as the solvent migrates by capillary action rather than forced flow [6] [4]. This translates to dramatically reduced solvent consumption – often 10-20 times less than equivalent HPLC methods [6].

Additionally, HPTLC-densitometry has significantly lower energy demands, operating at ambient temperature and pressure without requiring sophisticated pumping systems or column heaters [6]. The method also eliminates the need for expensive analytical columns and reduces waste generation, as sample preparation is typically minimal and no column cleaning or regeneration is necessary [5].

A comparative analysis of HPTLC versus HPLC for determining mupirocin in binary mixtures demonstrated HPTLC's superior green profile across multiple assessment metrics, including better Eco-Scale scores and improved AGREE evaluations [3].

Enhancing Green Credentials Through Method Optimization

The environmental performance of HPTLC-densitometry can be further improved through strategic optimization:

• Solvent Selection: Replacing traditional hazardous solvents like chloroform [7] with greener alternatives such as ethanol, heptane, or ethyl acetate [9] [6]
• Mobile Phase Optimization: Using minimal solvent volumes through systematic optimization approaches like PRISMA [5]
• Sample Throughput: Maximizing the number of samples analyzed per plate (typically 8-12 samples per plate) [6]

Diagram 1: HPTLC Environmental Advantages. The diagram illustrates the four key areas where HPTLC-densitometry demonstrates superior environmental performance compared to conventional chromatographic methods.

Detailed Experimental Protocols

Protocol 1: Green HPTLC-Densitometry for Abused Drugs Analysis

This protocol adapts a green HPTLC method for the simultaneous separation and quantification of structurally related abused drugs (tramadol, tapentadol, and venlafaxine) in seized pharmaceutical dosage forms [9].

Materials and Reagents:

• Stationary Phase: Pre-coated silica gel 60 F254 HPTLC plates (20 × 20 cm, 0.2 mm thickness)
• Mobile Phase: Heptane:acetone:ammonia (7:3:0.5, v/v/v)
• Reference Standards: Tramadol, tapentadol, and venlafaxine (pharmaceutical grade)
• Solvents: Heptane, acetone, ammonia (analytical grade)
• Sample Preparation: Methanol for extraction

Instrumentation:

• Sample Applicator: CAMAG Linomat 5 automatic applicator
• Development Chamber: CAMAG automatic developing chamber (ADC2)
• Scanner: CAMAG TLC scanner with winCATS software
• Syringe: 100 μL Hamilton syringe

Procedure:

• Standard Solution Preparation: Prepare individual stock solutions of 1 mg/mL in methanol. Prepare mixed standard solutions in the ratio 1:1:1.
• Sample Preparation: Extract powdered tablet equivalent to 10 mg of active ingredient with 10 mL methanol by sonication for 15 minutes. Filter through 0.45 μm membrane filter.
• Chromatographic Conditions:
  • Application volume: 10 μL as bands (8 mm width)
  • Application position: 10 mm from bottom edge
  • Development distance: 80 mm
  • Chamber saturation: 25 minutes with mobile phase
  • Development temperature: 25 ± 2°C
  • Relative humidity: 40 ± 5%
• Detection and Quantification:
  • Scanning wavelength: 272 nm in absorbance mode
  • Slit dimensions: 4.00 × 0.30 mm
  • Scanning speed: 100 nm/s
  • Evaluation via peak area against calibration curves

Method Validation:

• Linearity: 100-700 ng/band for all analytes
• Detection Limits: Tramadol (0.34 μg/band), tapentadol (0.16 μg/band), venlafaxine (0.084 μg/band)
• Precision: RSD ≤ 2% for intra-day and inter-day variations

Protocol 2: Green RP-HPTLC for Diosmin Analysis

This protocol describes a reversed-phase HPTLC method for determining diosmin in pharmaceutical formulations using green solvents [10].

Materials and Reagents:

• Stationary Phase: RP-18 silica gel 60 F254S HPTLC plates (10 × 20 cm)
• Mobile Phase: Ethanol:water (5.5:4.5, v/v)
• Reference Standard: Diosmin (pharmaceutical grade)
• Solvents: Ethanol, dimethyl sulfoxide (DMSO), methanol

Instrumentation:

• Sample Applicator: CAMAG Automatic TLC Sampler 4 (ATS4)
• Development Chamber: CAMAG automatic developing chamber (ADC2)
• Scanner: CAMAG TLC scanner with winCATS software

Procedure:

• Standard Solution Preparation: Prepare stock solution of 1 mg/mL in DMSO:methanol (3:7, v/v). Prepare working standards in mobile phase.
• Sample Preparation: For tablets, powder and extract equivalent to 500 mg diosmin with 20 mL DMSO, dilute to 100 mL with methanol. Filter and dilute appropriately.
• Chromatographic Conditions:
  • Application volume: 200 μL as bands (6 mm width)
  • Application rate: 150 nL/s
  • Development distance: 80 mm
  • Chamber saturation: 30 minutes at 22°C
• Detection and Quantification:
  • Scanning wavelength: 348 nm in absorbance/reflectance mode
  • Slit dimensions: 4 × 0.45 mm
  • Scanning speed: 20 mm/s

Method Validation:

• Linearity: 100-700 ng/band (R² = 0.9995)
• Accuracy: 98.5-101.5% recovery
• Precision: RSD < 2%

Greenness Assessment Protocol

A standardized approach to evaluating method greenness ensures consistent and comparable assessments across different analytical procedures.

Step 1: Data Collection

• Document all chemicals/reagents with amounts and hazard classifications
• Record energy consumption (instrument time, temperature requirements)
• Quantify waste generation (volume and composition)

Step 2: Tool Selection and Application

• Apply multiple assessment tools (minimum of three) for comprehensive evaluation
• Recommended tools: AGREE, GAPI, and Analytical Eco-Scale
• Utilize available software and calculators where possible

Step 3: Interpretation and Scoring

• Analytical Eco-Scale: Score > 75 (excellent greenness), > 50 (acceptable greenness)
• AGREE: Score > 0.75 (excellent greenness), > 0.5 (acceptable greenness)
• GAPI: Compare pictograms - fewer red sections indicate better greenness

Step 4: Comparative Analysis

• Compare scores with previously published methods for similar analyses
• Identify areas for improvement in greenness profile
• Document overall greenness classification

Table 2: Greenness Assessment Scores of Representative HPTLC Methods

Analytical Method	Analytical Eco-Scale	AGREE Score	NEMI	GAPI	Key Green Features
Green HPTLC for abused drugs [9]	>75 (Excellent)	>0.8	Not Reported	Not Reported	Heptane/acetone mobile phase, minimal solvent consumption
RP-HPTLC for apremilast [8]	93 (Excellent)	0.89 (Excellent)	Not Reported	Not Reported	Ethanol/water mobile phase, ethanol-based sample preparation
HPTLC for alkamides in Piper longum [5]	>75 (Excellent)	>0.8	Passed all criteria	Not Reported	Optimized solvent system, reduced chemical consumption
HPTLC for anti-migraine drugs [4]	Excellent (Spectrophotometry) > Acceptable (HPTLC)	Not Reported	Not Reported	Implemented	Method optimization for green solvent selection

Diagram 2: Greenness Assessment Workflow. This flowchart outlines the systematic four-step approach for evaluating the environmental impact of analytical methods using standardized metrics and tools.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for Green HPTLC-Densitometry

Item	Function/Application	Green Alternatives
HPTLC Plates (silica gel 60 F254, RP-18 F254S)	Stationary phase for separation	Standard commercially available options
Ethanol	Green solvent for mobile phase and extraction	Primary green solvent replacement for methanol, acetonitrile
Heptane	Green organic solvent for normal-phase separations	Alternative to hexane and other hazardous solvents
Ethyl Acetate	Biodegradable solvent for mobile phase	Replacement for chlorinated solvents
Water	Green solvent for reversed-phase systems	Solvent with minimal environmental impact
Automated Developing Chamber (CAMAG ADC2)	Controlled mobile phase development	Ensures reproducibility with minimal solvent use
TLC Scanner with winCATS software	Densitometric quantification at multiple wavelengths	Enables precise quantification without derivatization
Automatic Sample Applicator (CAMAG Linomat)	Precise sample application as bands	Improves reproducibility and reduces human error

The integration of Green Analytical Chemistry principles into pharmaceutical analysis through HPTLC-densitometry represents a significant advancement toward sustainable laboratory practices. The standardized protocols and comprehensive assessment frameworks presented in this document provide researchers with practical tools to implement environmentally responsible analytical methods without compromising analytical performance. As the field continues to evolve, the adoption of green assessment metrics will become increasingly important in method development and validation, ensuring that pharmaceutical analysis contributes positively to both public health and environmental protection.

High-Performance Thin-Layer Chromatography combined with densitometric detection (HPTLC-densitometry) represents a paradigm shift in sustainable analytical chemistry. This technique aligns with the core principles of Green Analytical Chemistry (GAC) by fundamentally redesigning analytical procedures to minimize environmental impact while maintaining analytical performance [6]. The inherent greenness of HPTLC-densitometry stems from its minimal solvent consumption, reduced energy requirements, and minimal waste generation compared to conventional chromatographic methods [11] [12]. The pharmaceutical industry and quality control laboratories are increasingly adopting HPTLC-densitometry not only for its economic advantages but for its superior ecological footprint, establishing it as a cornerstone technique for white analytical chemistry [12] [13].

This application note delineates the core principles establishing HPTLC-densitometry as an inherently green methodology, supported by quantitative sustainability metrics and practical implementation protocols for researchers and drug development professionals.

Core Green Principles of HPTLC-Densitometry

Minimal Solvent Consumption

The most significant environmental advantage of HPTLC-densitometry lies in its dramatically reduced solvent requirements. Unlike pump-based chromatography that continuously consumes mobile phase throughout analysis, HPTLC utilizes a fixed, small volume of mobile phase for development in a saturated chamber. This fundamental operational difference results in substantially reduced solvent waste [6].

A comparative analysis demonstrates that a typical HPTLC analysis consumes approximately 5-15 mL of mobile phase per sample, whereas conventional HPLC methods may require 500-1000 mL for comparable throughput [8]. This represents a reduction of 90-99% in solvent consumption. Furthermore, the development process in HPTLC occurs through capillary action, eliminating the energy-intensive high-pressure pumping systems required in HPLC [11].

Table 1: Solvent Consumption Comparison Between HPTLC and HPLC

Parameter	HPTLC-Densitometry	Conventional HPLC	Reduction
Mobile Phase Volume per Sample	5-15 mL	500-1000 mL	90-99%
Energy for Solvent Delivery	Capillary action (none)	High-pressure pumping	100%
Solvent Waste Generation	Minimal	Substantial	Significant

Reduced Energy Requirements

HPTLC-densitometry exhibits superior energy efficiency through multiple operational aspects. The technique eliminates the need for expensive analytical columns and sophisticated high-pressure pumping systems, which constitute the primary energy demands in liquid chromatography [6]. Sample analysis occurs at ambient temperature and pressure in HPTLC, further reducing energy consumption compared to temperature-controlled column compartments in HPLC [8].

The environmental impact of this reduced energy dependency is quantifiable through carbon footprint assessment. Recent studies calculating the carbon footprint of analytical methods reported values of 0.037 kg CO₂/sample for HPTLC-densitometry compared to significantly higher values for HPLC methods [6]. This substantial reduction in greenhouse gas emissions positions HPTLC as a climate-friendly alternative for routine analytical applications.

Minimal Waste Generation and Simplified Sample Preparation

HPTLC-densitometry generates minimal analytical waste through its operational design. The methodology typically requires small sample volumes (1-10 µL) and eliminates the need for extensive sample clean-up, reducing both chemical consumption and waste generation [14]. Furthermore, each HPTLC plate can accommodate multiple samples simultaneously (typically 10-15 samples/plate), significantly enhancing throughput while consolidating waste [6].

The sample preparation for HPTLC is notably simplified, often requiring only dissolution and filtration before application [14] [15]. This streamlined approach reduces the consumption of reagents, solvents, and energy associated with extensive sample preparation protocols common in other chromatographic methods. The cumulative effect is a substantial reduction in the environmental impact per analysis.

Alternative Detection Systems Enhancing Greenness

Recent advancements have introduced smartphone-based detection as an alternative to conventional densitometry, further enhancing the green profile of HPTLC [12] [13]. These innovative approaches utilize smartphone cameras coupled with image analysis software (e.g., ImageJ) or dedicated applications (e.g., Color Picker) for quantitative analysis of developed chromatograms [12].

This detection paradigm eliminates the need for energy-intensive scanning instrumentation, replacing it with widely available consumer electronics. The sustainability assessment of these hybrid methods using comprehensive metrics confirms their superior environmental profile compared to conventional instrumental methods [13]. Such innovations demonstrate how HPTLC methodology continues to evolve toward even greener implementations.

Quantitative Greenness Assessment

Multi-Metric Sustainability Evaluation

The greenness of HPTLC-densitometry methods has been rigorously evaluated using multiple validated assessment tools, providing quantitative confirmation of their environmental advantages. The Analytical GREEnness (AGREE) calculator scores HPTLC methods between 0.82-0.89 on a 0-1 scale, indicating excellent environmental performance [8] [6]. Similarly, the Analytical Eco-Scale frequently awards HPTLC methods scores above 90 (out of 100), classifying them as "excellent green" methods [8].

The Green Analytical Procedure Index (GAPI) tool further confirms the superior green profile of HPTLC-densitometry, with most methods displaying predominantly green sectors in their assessment pictograms [11]. More comprehensive evaluations using the White Analytical Chemistry (WAC) approach, which balances analytical, ecological, and practical criteria, have demonstrated that HPTLC methods achieve outstanding whiteness scores, confirming they successfully balance performance with sustainability [12] [13].

Table 2: Greenness Assessment Scores of HPTLC-Densitometry Methods

Assessment Tool	Scoring Range	Typical HPTLC Scores	Interpretation
AGREE	0-1 (1=excellent)	0.82-0.89	Excellent greenness
Analytical Eco-Scale	0-100 (>75=excellent)	90-93	Excellent greenness
GAPI	Pictogram (green/yellow/red)	Predominantly green	Low environmental impact
NQS (SDG Alignment)	Percentage	82-83%	Strong alignment with UN Sustainable Development Goals

Alignment with United Nations Sustainable Development Goals

Comprehensive sustainability assessment using the Need-Quality-Sustainability (NQS) index confirms that HPTLC-densitometry methods align with eleven UN Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production) [6]. This broad alignment demonstrates how HPTLC methodology supports global sustainability initiatives beyond laboratory-scale environmental benefits.

The cumulative evidence from these multi-faceted assessment tools establishes HPTLC-densitometry as a genuinely sustainable analytical approach, validated through rigorous, quantitative metrics rather than subjective claims.

Experimental Protocols for Green HPTLC-Densitometry

Protocol 1: Simultaneous Analysis of Aspirin and Metoclopramide

This protocol demonstrates a green HPTLC-densitometry method for analyzing compounds with challenging concentration ratios (90:1), showcasing the technique's capability for complex mixtures while maintaining environmental responsibility [11].

Materials and Reagents:

• HPTLC plates: Silica gel 60 F₂₅₄ (0.2 mm thickness)
• Mobile phase: Cyclo-hexane:Methanol:Methylene chloride (1:4:1, v/v/v)
• Standard solutions: Aspirin and Metoclopramide (1 mg/mL in methanol)
• Sample: Migramax oral powder
• Instrumentation: CAMAG HPTLC system with Linomat IV applicator and TLC Scanner 3

Methodology:

• Sample Preparation: Accurately weigh powder equivalent to 900 mg aspirin and 10 mg metoclopramide. Sonicate with 80 mL methanol for 30 minutes, filter, and dilute to 100 mL with methanol.
• Application: Apply 10 μL samples as 6 mm bands on HPTLC plates, 20 mm from bottom and 15 mm apart.
• Chromatographic Development: Develop in twin-trough chamber pre-saturated with mobile phase for 20 minutes at room temperature. Develop to a distance of 70 mm.
• Detection: Scan plates at 270 nm in reflectance-absorbance mode using densitometer.
• Quantification: Construct calibration curves (10-200 μg/band for aspirin; 1-45 μg/band for metoclopramide) and calculate concentrations in samples.

Greenness Assessment: This method achieved an excellent AGREE score and was classified as an "acceptable green method" using multiple assessment tools, with particular recognition for its minimal solvent consumption and waste generation [11].

Protocol 2: Smartphone-Based Detection for Naltrexone and Bupropion

This innovative protocol demonstrates the integration of smartphone technology with HPTLC to further enhance greenness by eliminating conventional energy-intensive detection systems [12].

Materials and Reagents:

• HPTLC plates: Silica gel 60 F₂₅₄ on aluminum (20 × 20 cm, 0.1 mm)
• Mobile phase: Ethyl acetate:Methanol:Acetone:Glacial acetic acid (3:6.5:1.5:0.5, by volume)
• Derivatization reagent: Modified Dragendorff's reagent
• Detection: Samsung Galaxy A70 smartphone (32 MP camera)
• Software: ImageJ (version 153) or Color Picker application (version 7.6.3)

Methodology:

• Chromatography: Apply samples as 6 mm bands, 1.5 cm from bottom edge. Develop in saturated glass tank with mobile phase.
• Derivatization: Dip developed plates in Dragendorff's reagent for 30 seconds, dry for 5 minutes, then spray with 5% w/v sodium nitrite solution.
• Image Capture: Place derivatized plates in illumination chamber. Capture images with smartphone camera from 15 cm distance under daylight illumination.
• Image Analysis:
  • ImageJ Method: Open image in ImageJ, define sample tracks with rectangular tool, generate intensity plots using "Gels" function, measure peak areas.
  • Color Picker Method: Open image in Color Picker app, select "Color Mask" function, adjust HSV parameters to isolate spots, measure intensity values.
• Quantification: Construct calibration curves using spot intensities (0.4-24 μg/band for naltrexone; 0.6-18 μg/band for bupropion).

Greenness Assessment: This method achieved outstanding scores in WAC assessment, particularly for its reduced energy consumption and equipment requirements, demonstrating how innovative detection approaches can further enhance HPTLC sustainability [12].

Essential Research Reagent Solutions

Table 3: Key Reagents and Materials for Green HPTLC-Densitometry

Reagent/Material	Function	Green Characteristics	Application Examples
Silica gel 60 F₂₅₄ plates	Stationary phase for separation	Reusable for method development, minimal material consumption	Analysis of pharmaceuticals [11], natural products [16]
Ethanol-Water mixtures	Green mobile phase components	Renewable, low toxicity, biodegradable	RP-HPTLC of apremilast [8]
Ethyl acetate-Methanol mixtures	Common normal-phase mobile phase	Lower toxicity compared to chlorinated solvents	Analysis of multiple drug combinations [12] [13]
Dragendorff's reagent	Derivatization for visualization	Allows use of smartphone detection, reducing energy consumption	Naltrexone and bupropion analysis [12]
ImageJ software	Image analysis for quantification	Free, accessible, eliminates specialized detection hardware	Smartphone-HPTLC methods [12] [13]

HPTLC-densitometry establishes itself as an inherently green analytical methodology through its fundamental operational principles: minimal solvent consumption, reduced energy requirements, minimal waste generation, and simplified sample preparation. The technique's environmental advantages are quantitatively confirmed through multiple assessment metrics, including AGREE, Analytical Eco-Scale, and GAPI. Furthermore, the ongoing innovation in HPTLC, particularly the integration of smartphone-based detection, continues to enhance its sustainability profile. For researchers and drug development professionals seeking to implement green chemistry principles without compromising analytical performance, HPTLC-densitometry offers a validated, practical approach that aligns with global sustainability initiatives and responsible laboratory practices.

The principles of Green Analytical Chemistry (GAC) have become a critical framework for promoting sustainable development within analytical laboratories. The core objective is to reduce the environmental impact of analytical practices, which often involve hazardous chemicals, significant energy consumption, and waste generation. This evolution necessitates robust, standardized tools to evaluate and compare the environmental footprint of analytical methods, enabling researchers to make informed decisions that align with sustainability goals. For researchers focused on High-Performance Thin-Layer Chromatography (HPTLC)-densitometry methods, applying these assessment tools is particularly relevant given the technique's consumption of solvents, materials, and energy. This overview details the foundational and emerging metrics—NEMI, AES, AGREE, GAPI, and MoGAPI—that provide structured approaches to quantify method greenness, each offering unique advantages for critical evaluation within pharmaceutical analysis and drug development contexts.

Detailed Profiles of Key Assessment Tools

National Environmental Methods Index (NEMI)

The National Environmental Methods Index (NEMI) is one of the older and more established tools for assessing the greenness of analytical procedures. Its profile symbol is divided into four quadrants, each representing a different environmental criterion: Persistence, Bioaccumulation, Toxicity, and Waste Generation. A field is colored green only if the chemical(s) used meet all the benchmark criteria for that category; otherwise, it remains blank. For instance, the toxicity quadrant is only colored green if all chemicals used have a Toxicity Characteristic Leaching Procedure (TCLP) value that is not classified as "D" (hazardous) and are not listed on the EPA's TRI (Toxic Release Inventory) list. The primary advantage of NEMI is its simplicity and ease of interpretation, providing a quick, at-a-glance overview. However, it suffers from significant drawbacks: it is not a quantitative tool, provides no information on the severity of environmental impact beyond pass/fail thresholds, and omits critical factors such as energy consumption and occupational hazards [17]. Its application is best suited for a preliminary, simplistic screening rather than a comprehensive evaluation.

Analytical Eco-Scale (AES)

The Analytical Eco-Scale (AES) is a semi-quantitative assessment tool that approaches greenness from a different perspective. It operates on a penalty points system, where an ideal green method starts with a base score of 100 points. Penalty points are then subtracted for each analytical parameter (e.g., reagents, solvents, energy consumption, waste generation) based on their amount, hazard, and environmental impact. The final score allows for a direct comparison between methods: a score higher than 75 is considered excellent green analysis, a score of 50-75 signifies acceptable green method, and a score below 50 represents inadequate greenness. This scoring system is a significant strength, as it facilitates ranking and comparison. Nevertheless, the AES also has limitations. It does not offer a visual representation of the assessment, and while it provides a total score, it lacks detailed information on the specific contribution of each analytical step to the overall environmental impact, making it difficult to identify the exact "weak points" in a procedure [18] [17].

Green Analytical Procedure Index (GAPI)

The Green Analytical Procedure Index (GAPI) was developed to address the limitations of earlier tools by providing a more comprehensive visual assessment of the entire analytical methodology. The GAPI symbol employs five colored pentagrams that correspond to major stages of the analytical process: sample collection, preservation, transportation, and storage; sample preparation; reagents and solvents used; instrumentation; and type of method and scale of operation. Each pentagram is subdivided, and each subsection is colored green, yellow, or red to represent low, medium, or high environmental impact, respectively. This detailed pictogram offers an immediate visual perspective on the greenness of each step, helping to quickly identify areas with the highest environmental burden. GAPI has gained wide acceptance due to its comprehensive scope and visual clarity. However, a notable drawback is that it does not generate a single, aggregated score, which can make a direct and objective comparison between two methods challenging, as one must qualitatively weigh multiple colored sections [17].

Modified GAPI (MoGAPI) and ComplexMoGAPI

To overcome the key limitation of the original GAPI, the Modified GAPI (MoGAPI) tool was recently developed. MoGAPI retains the familiar visual five-pentagram design of GAPI but introduces a crucial enhancement: a quantitative scoring system. This system assigns credits to the various options within each assessment criterion. The total credits are summed and divided by the maximum possible credits to calculate a percentage score. This overall score classifies methods into "excellent green" (≥75), "acceptable green" (50–74), or "inadequately green" (<50), similar to the Analytical Eco-Scale. The total score and overall evaluation are displayed on the chart, with the color of the scale around the pentagrams indicating the final rating. This evolution allows for both a detailed visual inspection and a precise quantitative comparison. Accompanying the tool is freely available, open-source software (bit.ly/MoGAPI) that simplifies and expedites the application of the MoGAPI metric [18]. A further extension, ComplexMoGAPI, has also been introduced, which builds upon the Complementary GAPI (ComplexGAPI) by incorporating a similar total scoring system. ComplexGAPI expands the assessment to include processes performed prior to the analytical procedure itself, and ComplexMoGAPI merges this visual appeal with precise total scores, also supported by open-source software (bit.ly/ComplexMoGAPI) [19].

AGREE Metric

The AGREE (Analytical GREEnness) metric is another recent and sophisticated tool that incorporates the 12 principles of GAC directly into its assessment framework. It utilizes a circular pictogram divided into 12 sections, each corresponding to one of the 12 principles. Each segment is assigned a score between 0 and 1, and the software automatically calculates an overall score based on these inputs, displayed in the center of the pictogram. The color of each segment shifts from red to yellow to green, providing an intuitive visual of the method's performance against each principle. The major strengths of AGREE include its comprehensive foundation in the 12 GAC principles, the provision of a single overall score for easy comparison, and the clear visual output that highlights both strengths and weaknesses. This makes it a powerful tool for a thorough and principled evaluation of analytical methods [18].

Table 1: Comparative Overview of Major Greenness Assessment Tools

Tool Name	Assessment Basis	Output Format	Quantitative Score?	Key Advantages	Main Limitations
NEMI	4 criteria: Persistence, Toxicity, etc.	4-quadrant pictogram	No	Simple, quick visual	No energy consideration; not quantitative
Analytical Eco-Scale (AES)	Penalty points for hazards	Single numerical score	Yes (0-100 scale)	Allows direct method ranking	No visual output; lacks detailed breakdown
GAPI	5 stages of analytical process	5 pentagrams with color codes	No	Comprehensive scope; visual	No total score for easy comparison
MoGAPI	Enhanced GAPI criteria	5 pentagrams + total score	Yes (Percentage)	Visual + quantitative score	Relatively new tool
AGREE	12 Principles of GAC	12-segment pictogram + score	Yes (0-1 scale)	Based on full GAC principles	Requires specialized software

Experimental Protocols for Greenness Assessment

Protocol for Applying the MoGAPI Tool

The Modified Green Analytical Procedure Index (MoGAPI) provides a structured protocol for evaluating the environmental impact of analytical methods, combining visual and quantitative outputs.

Step 1: Gather Method Details. Comprehensively collect all data related to the analytical procedure. This includes specifics on sample collection (e.g., in-line, online, offline), preservation, transportation, and storage conditions. For sample preparation, document every step—such as extraction, purification, and pre-concentration—including the types and volumes of solvents and reagents, and any waste generated. For the instrumental analysis, record the technique used (e.g., HPTLC, HPLC), instrument model, analysis time, and energy consumption per sample.

Step 2: Input Data into MoGAPI Software. Access the freely available, open-source MoGAPI software at bit.ly/MoGAPI. The software interface presents a series of questions and dropdown menus corresponding to the criteria in the five pentagrams. Input the collected method data into the relevant fields. For example, for the "Sample Preparation" section, you would select the type of extraction (if any), the quantity of solvent used, and its toxicity. If a step like "extraction" is not performed, select "Not Applicable (N/A)" to ensure that step is excluded from the total score calculation, preventing it from unfairly lowering the result [18].

Step 3: Generate and Interpret Results. After completing all input fields, the software automatically generates the MoGAPI pictogram and calculates the total greenness score. The output consists of two key components: 1) The Visual Pictogram: The five pentagrams, with each subsection colored green, yellow, or red, providing an immediate visual identification of the most and least environmentally friendly steps in the procedure. 2) The Total Percentage Score: A numerical value from 0 to 100%, calculated based on the sum of credits earned divided by the maximum possible credits (with N/A questions excluded). Interpret this score as follows: ≥75% signifies "Excellent Green," 50-74% signifies "Acceptable Green," and <50% signifies "Inadequately Green." The color of the scale surrounding the pentagrams will also reflect this overall assessment [18].

Protocol for AGREE Metric Assessment

The AGREE metric assessment is a robust protocol grounded in the 12 principles of Green Analytical Chemistry, facilitated by dedicated, open-source software.

Step 1: Download and Prepare. Download the AGREE metric software from the official repository (e.g., https://mostwiedzy.pl/AGREE). Familiarize yourself with the 12 principles of GAC, as each will form a basis for the assessment.

Step 2: Input and Weighting. Launch the software and input the required data for each of the 12 principles. The input is typically a score between 0 and 1, supported by justification based on the method's parameters. For instance, for Principle 1 (Direct analysis of samples without preparation), you would input a score of 1.0 if no sample preparation is needed, or a lower score if extensive preparation is required. For Principle 5 (Minimize derivatives), a score of 1.0 would be assigned if no derivatization is used. A key feature of the AGREE software is the ability to adjust the weighting of each principle. This allows the user to emphasize principles that are of greater importance for a specific application or context. The default setting is equal weighting for all principles [18].

Step 3: Analysis and Decision. Once all data and weightings are entered, the software generates the circular AGREE pictogram. The result displays a colored ring with 12 segments and the overall score in the center. Analyze this output by examining both the overall score (closer to 1.0 is better) and the color of individual segments. Segments colored red indicate significant environmental concerns related to that specific principle, guiding the researcher toward areas for potential method improvement or optimization. This makes AGREE not just an assessment tool, but also a guide for developing greener analytical methods [18].

The Scientist's Toolkit: Essential Reagents and Materials for HPTLC-Densitometry

The following table outlines key reagents, solvents, and materials commonly used in HPTLC-densitometry methods, with a focus on their function and role in greenness assessments. The choice of these items directly influences scores in tools like GAPI, AES, and AGREE.

Table 2: Key Research Reagent Solutions for HPTLC-Densitometry

Reagent/Material	Function in HPTLC-Densitometry	Greenness Considerations
Silica Gel HPTLC Plates	The stationary phase for compound separation.	Manufacturing process and waste disposal are key factors. Reusable plates are highly desirable from a green perspective.
Mobile Phase Solvents (e.g., Ethyl Acetate, Heptane, Methanol)	Liquid medium to carry and separate analytes up the plate.	This is a major focus. Volatility, toxicity, biodegradability, and sourcing (renewable vs. petroleum-based) are critically assessed. Safer solvent substitutes (e.g., ethanol instead of methanol) improve scores.
Derivatization Reagents (e.g., Anisaldehyde, Ninhydrin)	Chemical agents sprayed to visualize non-UV-active compounds.	Inherent toxicity, the quantity required, and the necessity of the derivatization step itself are penalized in green metrics (e.g., Principle 5 of AGREE).
Sample Solvents (e.g., Methanol, Chloroform)	Solvents used to dissolve the sample for application onto the plate.	Similar to mobile phase solvents, their hazard profile and volume used contribute significantly to the environmental impact, especially in waste generation.
Micro-Syringes or Automated Applicators	Devices for precise sample spotting onto the HPTLC plate.	Precision reduces the need for repeat analyses, minimizing solvent and material waste. Automated systems can enhance throughput and reduce human error.

Visualizing the Greenness Assessment Workflow

The following diagram illustrates the logical workflow for selecting and applying greenness assessment tools to an analytical method, culminating in iterative improvement.

Greenness Assessment and Optimization Workflow

The landscape of greenness assessment tools has evolved significantly, from simple pass/fail pictograms like NEMI to comprehensive, score-based frameworks like MoGAPI and AGREE. For researchers in HPTLC-densitometry and drug development, this suite of tools provides powerful means to quantify, justify, and communicate the environmental sustainability of their analytical methods. While each tool has its merits, the trend is clearly moving towards solutions that combine intuitive visual feedback with rigorous quantitative scoring, as demonstrated by MoGAPI and AGREE. Employing these metrics is no longer a niche pursuit but an integral part of modern, responsible analytical method development, ensuring that scientific progress aligns with the imperative of environmental protection.

In the evolving landscape of Green Analytical Chemistry (GAC), the need for robust, comprehensive metrics to evaluate method sustainability has become paramount. The Modified Green Analytical Procedure Index (MoGAPI) represents a significant advancement in greenness assessment tools, addressing critical limitations of its predecessor, the Green Analytical Procedure Index (GAPI). While GAPI provided a valuable visual assessment of environmental impact across five stages of analytical methodology through color-coded pentagrams, it lacked a crucial feature: a quantitative total score for straightforward method comparison [18] [20]. This limitation often made it challenging to objectively rank methods or track greenness improvements.

MoGAPI successfully bridges this gap by merging the visual interpretability of the traditional GAPI approach with the quantitative precision of scoring systems like the Analytical Eco-Scale [18]. Developed and introduced in 2024, this tool not only provides the characteristic red/yellow/green pictograms but also calculates an overall numerical assessment, enabling researchers to classify methods as excellent green (≥75), acceptable green (50-74), or inadequately green (<50) [18]. This evolution represents a critical step forward in standardizing environmental impact assessment across diverse analytical techniques, particularly for planar chromatography methods like HPTLC-densitometry that are central to pharmaceutical analysis.

MoGAPI Methodology and Scoring System

Fundamental Framework and Calculation

The MoGAPI tool employs a systematic scoring approach that evaluates multiple aspects of an analytical method's environmental impact. The calculation is based on assigning credits to various green characteristics across the analytical process, with the total credits summed and divided by the maximum possible credits to generate a percentage score [18]. Notably, if a particular question in the assessment is not applicable to a method, it is excluded from the total score calculation, ensuring the method can still achieve 100% if other green criteria are fully met [18].

The scoring system is designed to be comprehensive yet practical. For example, in sample collection, in-line collection receives the maximum score (3 credits), online collection receives an intermediate score, while offline collection receives the minimum score (1 credit) [18]. This granular approach ensures that genuinely greener practices are appropriately rewarded in the final assessment. The MoGAPI software, freely available as an open-source tool (bit.ly/MoGAPI), automates this scoring process, making greenness assessment accessible and consistent across different laboratories and applications [18].

Comparison with Other Greenness Assessment Tools

Table 1: Comparison of MoGAPI with Other Prominent Greenness Assessment Tools

Assessment Tool	Type of Output	Scoring System	Key Advantages	Primary Limitations
MoGAPI	Pictogram + numerical score	0-100% (Excellent: ≥75, Acceptable: 50-74, Inadequate: <50)	Combined visual and quantitative assessment; enables direct method comparison	Relatively new with fewer documented applications
GAPI	Color-coded pentagram (visual)	No overall score	Comprehensive visual assessment of entire analytical process	Lacks quantitative score for comparison
AGREE	Circular pictogram + numerical score	0-1 (with 1 being ideal)	Based on all 12 GAC principles; user-friendly software	Subjective weighting of criteria
Analytical Eco-Scale	Numerical score only	0-100 (Excellent: >75, Acceptable: 50-75, Inadequate: <50)	Simple calculation; facilitates direct comparison	Lacks visual component; penalty assignment can be subjective
NEMI	Simple pictogram	Binary (pass/fail for 4 criteria)	Very simple to use and interpret	Lacks granularity; doesn't assess full workflow

MoGAPI occupies a unique position in the landscape of greenness assessment tools by effectively balancing comprehensive evaluation with practical applicability. While tools like AGREE provide a foundation in the 12 principles of GAC, and the Analytical Eco-Scale offers straightforward numerical scoring, MoGAPI integrates the strengths of both approaches while maintaining the intuitive visual representation that made GAPI popular among researchers [20]. This multi-faceted assessment capability makes it particularly valuable for evaluating HPTLC-densitometry methods, where factors like solvent consumption, sample preparation requirements, and energy usage significantly influence overall environmental impact.

Application Protocols for HPTLC-Densitometry Methods

Step-by-Step Assessment Procedure

Implementing MoGAPI assessment for HPTLC-densitometry methods involves a systematic approach to ensure comprehensive and accurate evaluation:

• Method Documentation: Compile complete methodological details including sample preparation, stationary phase, mobile phase composition, development conditions, detection parameters, and waste management procedures [18] [21].

• Software Input: Access the MoGAPI web tool (bit.ly/MoGAPI) and input all relevant methodological parameters. The interface guides users through each category with pull-down menus and input fields for specific values [18].

• Parameter Categorization: Provide information across all relevant categories:

  • Sample Collection: Specify whether collection is in-line, online, or offline [18]
  • Sample Preservation and Transport: Detail any requirements for stabilization or transportation
  • Sample Preparation: Describe extraction methods, if applicable
  • Reagents and Solvents: Quantify amounts and identify hazard classifications
  • Instrumentation: Specify energy consumption per sample (kWh)
  • Occupational Hazards: Note any required safety controls
  • Waste Generation: Quantify waste volume and treatment procedures [18]

• Score Interpretation: Review the generated MoGAPI pictogram and numerical score. The color-coded output immediately highlights environmental strengths (green), moderate concerns (yellow), and significant issues (red), while the numerical score enables comparison with other methods [18].

HPTLC-Specific Assessment Considerations

For HPTLC-densitometry applications, several methodological aspects require particular attention during MoGAPI assessment:

• Mobile Phase Composition: The environmental impact of solvent systems must be evaluated, with greener solvents like ethanol receiving favorable scores compared to hazardous alternatives like chloroform or hexane [22] [21]. For example, a recently developed HPTLC method for Thioctic acid and Biotin utilized a mobile phase of chloroform:methanol:ammonia (8.5:1.5:0.05, by volume), which influenced its MoGAPI score of 76 [21].

• Sample Application Technology: Automated spray-on techniques typically yield better greenness scores than manual spotting due to improved reproducibility and reduced solvent consumption [23].

• Development Technique: The chamber saturation time, development distance, and temperature control all contribute to energy consumption and solvent usage [22].

• Detection Method: Densitometric detection parameters, including source lamp type, scanning speed, and slit dimensions, influence energy consumption per analysis [22] [24].

The following workflow diagram illustrates the systematic process for applying MoGAPI assessment to HPTLC-densitometry methods:

MoGAPI Assessment Workflow for HPTLC Methods

Case Studies and Experimental Data

Pharmaceutical Combination Analysis

Recent applications demonstrate MoGAPI's utility in assessing the greenness of HPTLC methods for pharmaceutical analysis. In one case study, a stability-indicating HPTLC method for the simultaneous analysis of Dapagliflozin propanediol monohydrate and Bisoprolol fumarate was developed and evaluated [22]. The method employed HPTLC silica gel 60 F₂₅₄ plates with a mobile phase of Chloroform:Toluene:Methanol:Ammonia (1:2:6:0.1 v/v/v) and detection at 224 nm. The method demonstrated excellent linearity (correlation coefficients of 0.9995 and 0.9991) over ranges of 200-1200 ng/band for Dapagliflozin and 100-600 ng/band for Bisoprolol fumarate, with precision demonstrated by percentage relative standard deviation values below 2% [22].

The MoGAPI assessment of this method provided valuable insights into its environmental profile, particularly highlighting the impact of solvent selection on overall greenness. The chloroform-containing mobile phase represented a significant environmental concern, while other aspects of the HPTLC methodology contributed favorably to the overall score [22]. This case illustrates how MoGAPI helps identify specific areas for potential improvement while validating the overall acceptability of the method's environmental impact.

Comprehensive Sustainability Assessment

Another illustrative application involved the development of an HPTLC method for Thioctic acid and Biotin, where a tri-faceted sustainability assessment was performed [21]. The method achieved a MoGAPI score of 76, complemented by an Analytical Eco-Scale score of 80 and an AGREE score of 0.72, providing a comprehensive view of its environmental performance [21]. This multi-metric approach demonstrates how MoGAPI can be integrated with other assessment tools to obtain a robust evaluation of method greenness.

Table 2: Experimental Parameters from HPTLC Method Case Studies

Analytical Target	Chromatographic Conditions	Validation Parameters	Greenness Scores
Vonoprazan + Aspirin [25]	HPLC: C18 column, phosphate buffer pH 6.8:ACN (63:37), 230 nmHPTLC: ethyl acetate:ethanol(75%):ammonia (5:5:0.05)	Linearity: 0.5-10 µg/mL (VON), 1-100 µg/mL (ASP)LOD: 0.17 µg/mL (VON), 0.33 µg/mL (ASP)	AGREE, Complementary GAPI, and RGB 12-model assessments confirmed greenness
Dapagliflozin + Bisoprolol [22]	HPTLC silica gel 60 F₂₅₄, Chloroform:Toluene:Methanol:Ammonia (1:2:6:0.1 v/v/v), 224 nm	Linearity: 200-1200 ng/band (DAPA), 100-600 ng/band (BSF)Recovery: 98.21-100.08% (DAPA), 99.19-100.15% (BSF)	MoGAPI tool applied for greenness assessment
Thioctic acid + Biotin [21]	HPTLC silica gel 60 F₂₅₄, Chloroform:Methanol:Ammonia (8.5:1.5:0.05), 215 nm	Linearity: 2.5-30 µg/band (TH), 2.5-20 µg/band (BO)LOD: 0.58 µg/band (TH), 0.33 µg/band (BO)	MoGAPI: 76, Eco-Scale: 80, AGREE: 0.72

The quantitative data from these case studies demonstrates how MoGAPI scoring effectively complements traditional method validation parameters, providing researchers with a comprehensive picture of both analytical performance and environmental sustainability.

Essential Research Reagent Solutions

Successful implementation of green HPTLC-densitometry methods requires careful selection of reagents and materials to optimize both analytical performance and environmental profile. The following table details key research reagent solutions and their functions in method development:

Table 3: Essential Research Reagents for Green HPTLC-Densitometry

Reagent/Material	Function in HPTLC Analysis	Green Considerations	Application Example
Silica Gel 60 F₂₅₄ Plates	Stationary phase for chromatographic separation	Reusable with modification; minimal waste generation	Standard substrate for pharmaceutical analysis [22] [21]
Ethanol	Green solvent for mobile phase and sample preparation	Biodegradable; low toxicity; renewable source	Used in ethyl acetate:ethanol:ammonia mobile phase [25]
Ethyl Acetate	Medium-polarity solvent for mobile phase	Relatively low toxicity compared to chlorinated solvents	Mobile phase component for Vonoprazan and Aspirin analysis [25]
Methanol	Organic modifier for mobile phase	Higher toxicity than ethanol but often required for solubility	Component in chloroform:methanol:ammonia systems [22] [21]
Ammonia Solution	Modifier for controlling selectivity and spot shape	Volatile; minimal residue; effective at low concentrations	Used in minute quantities (0.05-0.1 parts) in mobile phases [25] [22]
Water	Green solvent for sample preparation	Non-toxic; renewable; inexpensive	Primary solvent in HPLC-based methods [25]
Phosphate Buffer	Mobile phase modifier for pH control	Low environmental impact at appropriate concentrations	Used in HPLC analysis of Vonoprazan and Aspirin [25]

The strategic selection of these reagents directly influences MoGAPI scores, with greener alternatives like ethanol receiving more favorable assessments than hazardous solvents like chloroform. Method development should prioritize these greener alternatives where possible while maintaining the necessary analytical performance for reliable pharmaceutical analysis.

Comparative Analysis and Method Selection Guidance

The relationship between analytical performance and environmental impact represents a critical consideration in modern method development. The following diagram illustrates how MoGAPI facilitates balanced method selection by integrating greenness assessment with practical analytical requirements:

Method Selection Integrating MoGAPI Assessment

MoGAPI excels in this comparative context by providing both the visual representation to quickly identify environmental hotspots and the numerical score to objectively rank alternatives. When evaluating HPTLC-densitometry methods, researchers should consider:

• Solvent Selection Impact: Mobile phase composition typically represents the most significant factor in MoGAPI scoring. Where possible, methods should utilize ethanol-water systems or other greener alternatives before resorting to more hazardous solvents [25] [26].

• Energy Efficiency Considerations: HPTLC methods generally demonstrate favorable energy profiles compared to HPLC techniques due to shorter analysis times and lower instrumental energy demands [23]. This advantage is appropriately captured in MoGAPI assessment.

• Waste Management Strategies: Methods that incorporate waste minimization, recycling, or treatment procedures achieve better MoGAPI scores. HPTLC inherently generates less waste than many chromatographic techniques, with typical mobile phase consumption below 10 mL per analysis [23].

• Throughput Considerations: The ability to analyze multiple samples simultaneously on a single HPTLC plate significantly improves the environmental profile when calculated per sample, a factor recognized in MoGAPI assessment [23].

By applying MoGAPI scoring during method development rather than as a final assessment tool, researchers can iteratively improve the environmental profile of HPTLC-densitometry methods while maintaining the necessary analytical performance for pharmaceutical applications.

The MoGAPI tool represents a significant advancement in greenness assessment methodology, effectively addressing the limitations of previous tools while maintaining the visual intuitiveness that facilitates widespread adoption. For researchers focused on HPTLC-densitometry method development, this tool provides a robust framework for evaluating and improving environmental sustainability while maintaining analytical performance. The integration of quantitative scoring with pictorial representation enables both quick assessment and objective comparison, supporting the pharmaceutical industry's growing commitment to Green Analytical Chemistry principles. As demonstrated through multiple case studies, MoGAPI offers practical value in method development, optimization, and selection, making it an essential component of modern analytical quality by design approaches.

The field of analytical chemistry is increasingly embracing the principles of sustainability, moving beyond sole reliance on Green Analytical Chemistry (GAC). A more holistic approach, White Analytical Chemistry (WAC), has emerged, evaluating methods not only on their environmental impact (greenness) but also on their analytical practicality (blueness) and economic and operational feasibility (whiteness). While greenness assessment tools for High-Performance Thin-Layer Chromatography-densitometry (HPTLC-densitometry) methods, such as AGREE and AES, are well-established, the metrics for blueness and whiteness are less familiar to many researchers [27] [28]. This document provides a detailed introduction to the Blue Applicability Grade Index (BAGI) and the Red-Green-Blue 12 (RGB12) algorithm, framing them within the broader context of a comprehensive sustainability assessment for HPTLC-densitometry methods in pharmaceutical research.

Beyond Greenness: The Pillars of White Analytical Chemistry

White Analytical Chemistry integrates the three key dimensions of method sustainability [28]:

• Greenness: Environmental impact, including waste generation, energy consumption, and toxicity of reagents.
• Blueness: Analytical performance and practicality, encompassing factors like accuracy, precision, analysis time, and detection limits.
• Whiteness: Economic and operational benefits, such as cost-effectiveness, time-efficiency, and user-friendliness.

A truly sustainable method must perform well in all three areas. The relationship between these concepts and the workflow for a comprehensive assessment is outlined below.

Key Assessment Tools and Metrics

The Blue Applicability Grade Index (BAGI)

BAGI is a metric designed to quantitatively evaluate the practicality and applicability of an analytical method. It assigns a score based on key performance characteristics, with a higher score indicating a more robust and user-friendly method [28].

Protocol for Calculating the BAGI Score:

• Data Collection: Gather validation data for the HPTLC-densitometry method as per International Council for Harmonisation (ICH) guidelines [29] [27]. Essential parameters include:

  • Linearity range and correlation coefficient (r)
  • Limit of Detection (LOD) and Limit of Quantification (LOQ)
  • Precision (expressed as % Relative Standard Deviation, %RSD)
  • Accuracy (expressed as % Recovery)
  • Robustness

• Scoring: Input these parameters into the BAGI calculator. The tool assigns points for each parameter based on its performance against established benchmarks. For instance, a wider linearity range, lower LOD/LOQ, and higher precision contribute to a higher score.

• Interpretation: The final score is interpreted on a scale. A BAGI score of 80, as achieved by an RP-TLC method for monosodium glutamate, indicates a high level of applicability and suggests the method is suitable for routine use in quality control laboratories [28].

The RGB12 Algorithm

The RGB12 algorithm is a unified metric that calculates a single percentage score representing a method's overall performance across the red (economic), green (environmental), and blue (analytical) dimensions. A score of 100% represents an ideal method that is cost-effective, environmentally benign, and analytically sound [28].

Protocol for Calculating the RGB12 Score:

• Prerequisite Assessment: First, determine the method's greenness score using a tool like AGREE or GAPI, and its blueness score using BAGI.

• Input: The RGB12 algorithm integrates these scores along with an assessment of economic and operational factors (e.g., cost of reagents, equipment, analysis time, sample throughput).

• Calculation and Output: The algorithm processes these inputs to generate a final percentage score. For example, an AQbD-assisted RP-TLC method for monosodium glutamate achieved an RGB12 score of 85.1%, demonstrating a strong balance between economic benefit, ecological safety, and practical utility [28].

Comparison of Key Greenness, Blueness, and Whiteness Metrics

The following table summarizes the core assessment tools discussed, allowing for direct comparison of their focus and output.

Table 1: Comparison of Sustainability Assessment Metrics for Analytical Methods

Metric Tool	Primary Focus	Core Principle	Output / Score	Ideal Outcome
AGREE [8] [27]	Greenness	Environmental Impact	0 to 1 scale	Score of 1 (Excellent greenness)
AES [8] [27]	Greenness	Environmental Impact	Penalty points (100 = ideal)	Score of 100 (Excellent greenness)
BAGI [28]	Blueness	Analytical Practicality & Performance	Points-based scale	High score (e.g., 80 = High applicability)
RGB12 [28]	Whiteness	Holistic Balance (Economic, Green, Blue)	Percentage (%)	100% (Perfect balance)

Experimental Protocol: Implementing a Whiteness and Blueness Assessment for an HPTLC-Densitometry Method

This protocol provides a step-by-step guide for evaluating a reversed-phase HPTLC-densitometry method used for the quantification of a pharmaceutical compound (e.g., Ertugliflozin, Apremilast) in tablets [8] [27].

Materials and Reagents

Table 2: Essential Research Reagent Solutions and Materials

Item	Function / Role in HPTLC-Densitometry
RP-18 Silica gel 60 F254S HPTLC Plates	The stationary phase for reversed-phase chromatographic separation.
Green Solvents (e.g., Ethanol, Water)	Components of the mobile phase; chosen for their lower environmental toxicity compared to traditional solvents like chloroform [8] [27].
Standard Compound (e.g., API)	The pure active pharmaceutical ingredient used to prepare calibration standards for quantitative analysis.
Sample Solutions (e.g., Tablet Extract)	The prepared sample containing the analyte of interest for quantification.
Densitometry Scanner	Instrument for measuring the intensity of the analyte bands on the HPTLC plate post-development for quantification.
Chromatographic Chamber	A sealed tank for the development of HPTLC plates with the mobile phase.

Procedure

• Method Development and Validation:

  • Develop the HPTLC method by optimizing the mobile phase composition (e.g., Ethanol/Water, 80:20 v/v) and chromatographic conditions [27].
  • Validate the method according to ICH Q2(R2) guidelines [29] [27]. Key parameters to determine include:
    • Linearity: Across a defined range (e.g., 25–1200 ng/band) with a correlation coefficient (r).
    • Precision: Intra-day and inter-day precision expressed as %RSD (should typically be < 2%) [29].
    • Accuracy: Via recovery studies (e.g., 98-102%).
    • LOD and LOQ: Calculated based on the standard deviation of the response and the slope of the calibration curve [29].

• Greenness Assessment:

  • Input the method parameters (e.g., reagent toxicity, energy consumption, waste amount) into a greenness tool such as AGREE [8] [27]. Record the final score on a 0-1 scale.

• Blueness Assessment:

  • Input the validation data (linearity, LOD, LOQ, precision, accuracy, robustness) obtained in Step 1 into the BAGI calculator [28]. Record the final applicability score.

• Whiteness Assessment:

  • Use the RGB12 algorithm, incorporating the greenness score (from Step 2), the blueness score (from Step 3), and economic data (e.g., cost per analysis, analysis time, instrument complexity) [28]. Record the final whiteness percentage.

• Data Synthesis and Comparison:

  • Compare the BAGI and RGB12 scores with those of alternative methods (e.g., normal-phase HPTLC or HPLC) to make an informed decision on the most sustainable and practical method.

Integrating whiteness and blueness assessments with traditional greenness evaluation represents the future of sustainable analytical science. Tools like BAGI and RGB12 provide researchers and drug development professionals with a standardized, quantitative framework to select methods that are not only environmentally friendly but also analytically superior and economically viable. The adoption of this holistic White Analytical Chemistry approach ensures that HPTLC-densitometry methods contribute effectively to the development of greener pharmaceuticals without compromising on quality or practicality.

Implementing Sustainable HPTLC Methods in Pharmaceutical Analysis

The adoption of Green Analytical Chemistry (GAC) principles in pharmaceutical analysis represents a critical evolution toward sustainable laboratory practices. High-performance thin-layer chromatography-densitometry (HPTLC-densitometry) has emerged as a frontrunner in this transition, primarily due to its significantly lower solvent consumption per sample analyzed compared to conventional HPLC methods. The strategic selection of mobile phase solvents constitutes the most impactful variable in reducing the environmental footprint of HPTLC methods, as mobile phases account for the majority of waste generated in chromatographic laboratories.

This application note frames green mobile phase selection within the broader context of academic research on greenness assessment of HPTLC-densitometry methods. We provide evidence-based protocols, quantitative comparisons, and implementation strategies to enable researchers and pharmaceutical analysts to systematically replace hazardous solvents with safer alternatives without compromising analytical performance.

Strategic Framework for Green Solvent Selection

Green Solvent Assessment Tools and Principles

Modern green solvent selection is guided by standardized assessment tools that evaluate environmental, health, and safety parameters:

• AGREE (Analytical GREEnness Metric): Provides a comprehensive score from 0 to 1 based on all 12 principles of GAC, with higher scores indicating superior greenness [30] [12].
• GAPI (Green Analytical Procedure Index): A pictogram that evaluates method steps across five categories with color-coded assessment (green-yellow-red) [12] [4].
• Analytical Eco-Scale: A scoring system where higher scores (closer to 100) indicate excellent greenness [8] [4].
• NEMI (National Environmental Methods Index): A simple pictogram indicating whether a method meets basic green criteria [31].

These tools consistently identify ethanol, water, acetone, and ethyl acetate as preferred green solvents due to their favorable environmental and toxicity profiles compared to traditional chromatographic solvents like chloroform, acetonitrile, and n-hexane [32].

Solvent Replacement Guidelines

Table 1: Strategic Solvent Replacement Guide for HPTLC Mobile Phases

Hazardous Solvent	Recommended Green Alternative	Key Considerations	Reported Greenness Metrics
Chloroform	Ethyl acetate/ethanol mixtures	Lower toxicity, biodegradable	AGREE: 0.83 [30]
Acetonitrile	Ethanol	Renewable source, lower toxicity	AGREE: 0.89 [8]
Methanol	Ethanol	Less toxic, renewable	Analytical Eco-Scale: 93 [8]
n-Hexane	Heptane or cyclohexane	Less toxic, safer handling	GAPI: Improved profile [4]
Dichloromethane	Acetone/water mixtures	Significantly lower toxicity	ChlorTox: 0.66 g [8]

Experimental Protocols for Method Development and Transition

Protocol 1: Direct Replacement and Optimization for Reversed-Phase HPTLC

This protocol outlines the systematic development of green reversed-phase HPTLC methods, particularly suitable for pharmaceutical compounds with moderate to high polarity.

Materials and Reagents:

• HPTLC Plates: RP-18 silica gel 60 F254S plates
• Green Solvents: Ethanol (HPLC grade), acetone (HPLC grade), purified water
• Reference Standards: Pharmaceutical compounds of interest
• Application Device: Automated HPTLC applicator (e.g., Camag Linomat series)
• Development Chamber: Standard twin-trough glass chamber
• Detection: Densitometer with UV/Vis capability

Procedure:

• Initial Solvent Screening: Prepare mobile phases with ethanol/water or acetone/water in ratios ranging from 50:50 to 80:20 (v/v).
• Plate Preparation: Pre-wash RP-18 plates with methanol and activate at 110°C for 5 minutes.
• Sample Application: Apply standard solutions as 6-8 mm bands with appropriate spacing.
• Chromatographic Development: Develop in pre-saturated chambers (15-25 minute saturation) to a distance of 70-80 mm.
• Optimization Iterations: Systematically adjust solvent ratios in 5% increments to achieve optimal resolution (Rf 0.2-0.8).
• Validation: Confirm specificity, linearity, and precision per ICH Q2(R2) guidelines.

Exemplar Case: A green RP-HPTLC method for apremilast quantification utilized ethanol/water (65:35, v/v) as mobile phase, achieving excellent performance (Rf = 0.61 ± 0.01) with outstanding greenness metrics (AGREE = 0.89, Analytical Eco-Scale = 93) [8].

Protocol 2: Normal-Phase HPTLC with Green Solvent Systems

For compounds requiring normal-phase separation, this protocol provides a pathway to replace hazardous solvents.

Materials and Reagents:

• HPTLC Plates: Silica gel 60 F254
• Green Solvents: Ethyl acetate, ethanol, ethyl lactate, acetone, heptane
• Additives: Glacial acetic acid, ammonia solution (for pH adjustment)

Procedure:

• Binary System Screening: Test ethyl acetate/ethanol and ethyl acetate/acetone combinations in varying proportions.
• Ternary System Development: For challenging separations, incorporate modifiers like glacial acetic acid (1-3%) or ammonia (0.1-1%).
• Development Chamber Conditioning: Standardize saturation time (20-30 minutes) and development distance.
• Detection Optimization: Employ UV detection at compound-specific λmax or post-chromatographic derivatization for non-UV-absorbing compounds.

Exemplar Case: Analysis of bisoprolol fumarate and amlodipine besylate with a mutagenic impurity employed ethyl acetate-ethanol (7:3, v/v) mobile phase, achieving baseline separation with excellent greenness profiles (AGREE > 0.8) [31].

Quantitative Comparison of Conventional versus Green HPTLC Methods

Table 2: Performance Comparison of Conventional vs. Green HPTLC Methods

Analytical Parameter	Conventional NP-HPTLC (Chloroform/Methanol)	Green RP-HPTLC (Acetone/Water)	Improvement with Green Method
Linearity Range	40-400 ng/band [33]	30-800 ng/band [33]	100% wider linear range
Determination Coefficient (R²)	0.9985 [33]	0.9995 [33]	Improved correlation
LOD/LOQ	13.52/40.56 ng/band [33]	10.30/30.90 ng/band [33]	Lower detection limits
Accuracy (% Recovery)	95.54-97.16% [33]	98.27-100.85% [33]	Enhanced accuracy
Precision (% RSD)	<2% [22]	<2% [22]	Comparable precision
Environmental Impact	High (chloroform toxicity) [33]	Low (green solvents) [33]	Significantly reduced

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagents and Materials for Green HPTLC Method Development

Item	Function/Application	Green Considerations
RP-18 HPTLC Plates	Stationary phase for reversed-phase separations	Enables use of aqueous mobile phases
Silica Gel 60 F254 HPTLC Plates	Standard normal-phase stationary phase	Compatible with greener organic solvents
Ethanol (HPLC Grade)	Primary green solvent for RP and NP systems	Renewable, low toxicity, biodegradable
Acetone (HPLC Grade)	Green solvent for RP systems	Excellent chromatographic properties
Ethyl Acetate	Green solvent for normal-phase systems	Lower toxicity than chloroform/DCM
Ethyl Lactate	Bio-based green solvent	Renewable source, biodegradable
Automated Sample Applicator	Precise sample application (e.g., Camag Linomat)	Reduces solvent consumption through accuracy
Twin-Trough Development Chamber	Mobile phase development with pre-saturation	Standardizes separation conditions

Implementation Workflow and Decision Framework

The transition to green mobile phases follows a logical progression from assessment to implementation. The diagram below illustrates this workflow, incorporating key decision points and optimization cycles.

Emerging Trends and Future Directions

Smartphone-Assisted Detection for Enhanced Sustainability

Recent advances integrate smartphone detection with HPTLC to further reduce environmental impact and improve accessibility. This approach eliminates the need for expensive densitometry equipment while maintaining analytical reliability:

• ImageJ Software Analysis: Open-source image processing quantifies band intensity from smartphone-captured TLC plate images [12] [24].
• Color Picker Applications: Mobile apps provide quantitative analysis of derivatized spots with minimal equipment investment [12].
• Sustainability Metrics: Smartphone-HPTLC methods demonstrate superior scores in comprehensive greenness assessments (AGREE > 0.8) while reducing capital costs [24].

Advanced Green Assessment Integration

Future method development will increasingly incorporate multi-criteria assessment tools that simultaneously evaluate greenness, practicality, and analytical performance:

• White Analytical Chemistry (WAC): Integrates environmental impact with methodological and practical considerations using an RGB model [12] [31].
• Multi-Tool Verification: Comprehensive assessment using AGREE, GAPI, and Analytical Eco-Scale provides complementary perspectives on method sustainability [30] [4].
• Lifecycle Analysis: Emerging approaches consider the complete environmental impact from solvent production to waste disposal [32].

The strategic selection of green mobile phases represents a paradigm shift in HPTLC-densitometry that aligns with the core principles of Green Analytical Chemistry. As demonstrated through the protocols and data presented herein, green solvent systems consistently achieve equivalent or superior analytical performance compared to conventional methods while significantly reducing environmental impact and safety hazards. The integration of systematic method development protocols with comprehensive greenness assessment tools provides researchers with a robust framework for implementing sustainable chromatographic practices that meet both analytical and environmental objectives.

The pharmaceutical industry is increasingly embracing the principles of Green Analytical Chemistry (GAC) to develop analytical methods that minimize environmental impact while maintaining scientific validity. High-Performance Thin-Layer Chromatography (HPTLC)-densitometry presents an ideal platform for this integration, offering reduced solvent consumption and faster analysis times compared to conventional techniques. This case study details the development and validation of a novel, green HPTLC-densitometry method for the simultaneous analysis of dapagliflozin propanediol monohydrate (DAPA) and bisoprolol fumarate (BSF) in a combined oral formulation, utilizing a mobile phase of chloroform:toluene:methanol:ammonia (1:2:6:0.1 v/v/v) [22].

The combination of DAPA, a sodium-glucose co-transporter 2 inhibitor, and BSF, a beta-blocker, represents a therapeutic strategy for managing patients with type 2 diabetes mellitus and concomitant cardiovascular conditions such as hypertension. The development of a robust analytical method for this combination is crucial for pharmaceutical quality control and stability testing. The methodology described herein was validated as per the International Council for Harmonisation (ICH) Q2(R2) guidelines and its environmental impact was critically evaluated using the Modified Green Analytical Procedure Index (MoGAPI) tool, aligning with the broader thesis research on greenness assessment in analytical science [22].

Experimental Protocols

Materials and Reagents

• Active Pharmaceutical Ingredients (APIs): Dapagliflozin propanediol monohydrate (99.70%) and Bisoprolol fumarate (99.35%) were obtained from pharmaceutical manufacturers [22].
• Pharmaceutical Formulation: The method was applied to analyze commercially available tablets (e.g., Corbis D) containing 10 mg of DAPA and 5 mg of BSF per tablet [22].
• Chemicals and Solvents: Methanol, chloroform, toluene, and ammonia solution were used. All solvents were of analytical or chromatographic grade [22].
• HPTLC Plates: Pre-coated silica gel 60 F₂₅₄ aluminum plates (10 cm × 10 cm, 0.20 mm thickness) from E. Merck, Germany [22].

Instrumentation and Chromatographic Conditions

The following table summarizes the key instrumentation and conditions used:

• Sample Applicator: Camag Linomat 5 auto-sampler with a 100 µL syringe [22]
• Development Chamber: 10 × 10 cm twin-trough glass chamber [22]
• Scanner: Camag TLC Scanner 3 [22]
• Software: winCATS version 1.3.0 [22]
• Detection Wavelength: 224 nm [22]
• Mobile Phase: Chloroform: Toluene: Methanol: Ammonia (1:2:6:0.1 v/v/v) [22]
• Saturation Time: 30 minutes at room temperature (25 ± 2 °C) [22]
• Development Distance: 80 mm [22]
• Band Length: 6 mm [22]

Preparation of Standard and Sample Solutions

• Standard Stock Solutions: Precisely weigh 100 mg of DAPA and 50 mg of BSF. Transfer to a 100 mL volumetric flask, dissolve in and dilute to volume with methanol to obtain a combined stock solution with concentrations of 1000 µg/mL for DAPA and 500 µg/mL for BSF [22].
• Working Standard Solutions: Pipette 2 mL of the combined stock solution into a 100 mL volumetric flask and dilute to volume with methanol to achieve working concentrations of 20 µg/mL for DAPA and 10 µg/mL for BSF [22].
• Sample Solution (Tablet Extraction): Weigh and finely powder twenty tablets. Accurately weigh a portion of the powder equivalent to 10 mg of DAPA and 5 mg of BSF and transfer to a 100 mL volumetric flask. Add approximately 50 mL of methanol, sonicate for 5 minutes, then dilute to volume with methanol. Filter the solution to remove insoluble excipients before application [22].

Method Validation Procedure

The developed method was validated according to ICH Q2(R2) guidelines for the following parameters [22]:

• Linearity: Prepare six concentrations of DAPA (200–1200 ng/band) and BSF (100–600 ng/band) from working standards. Apply each concentration in triplicate on the HPTLC plate, develop, and scan. Plot the average peak area versus the corresponding concentration for each drug and calculate the regression equation and correlation coefficient (r).
• Precision:
  • Intra-day Precision: Analyze three different concentrations (low, medium, high) of DAPA and BSF in triplicate on the same day. Calculate the % Relative Standard Deviation (%RSD) of the peak areas.
  • Inter-day Precision: Repeat the intra-day procedure over three consecutive days. Calculate the %RSD of the peak areas.
• Accuracy (Recovery Studies): Conduct a standard addition method at three concentration levels (e.g., 80%, 100%, 120%). Add a known amount of pure DAPA and BSF to a pre-analyzed sample solution. Analyze the spiked samples and calculate the percentage recovery of the added standard.
• Specificity: Confirm the specificity by comparing the chromatograms of the standard drugs and the sample solution. The method should demonstrate good separation of both analytes from any potential degradation products or excipients, confirmed by determining the Rf values.
• Forced Degradation (Stability-Indicating Property): Subject the sample solution to various stress conditions including acid hydrolysis (e.g., 0.1M HCl), base hydrolysis (e.g., 0.1M NaOH), oxidative degradation (e.g., 3% H₂O₂), and thermal degradation. After inducing degradation, analyze the samples to demonstrate the separation of degradation products from the parent drugs, thus proving the stability-indicating property of the method.

HPTLC Method Workflow: This diagram illustrates the step-by-step experimental procedure for the green HPTLC-densitometry method, from mobile phase optimization to final application.

Results and Data Analysis

Method Validation and Performance Data

The developed HPTLC method was comprehensively validated. The quantitative data presented in the tables below confirm the method's reliability for the simultaneous analysis of DAPA and BSF.

Table 1: System Suitability and Validation Parameters for DAPA and BSF [22]

Parameter	Result for Dapagliflozin (DAPA)	Result for Bisoprolol Fumarate (BSF)
Retention Factor (Rf)	0.22 ± 0.003	0.63 ± 0.006
Linearity Range	200–1200 ng/band	100–600 ng/band
Correlation Coefficient (r)	0.9995	0.9991
Precision (%RSD)
Intra-day	< 2%	< 2%
Inter-day	< 2%	< 2%
Accuracy (% Recovery)	98.21 – 100.08%	99.19 – 100.15%

Table 2: Forced Degradation Profile of DAPA and BSF [22]

Stress Condition	Observation for Dapagliflozin (DAPA)	Observation for Bisoprolol Fumarate (BSF)
Acidic Hydrolysis	Highly susceptible; significant degradation	Less susceptible; stable
Oxidative Hydrolysis	Highly susceptible; significant degradation	Less susceptible; stable
Basic Hydrolysis	Susceptible to degradation	Stable
Thermal Degradation	Stable	Stable
Photolytic Degradation	Stable	Stable

Greenness Assessment Using the MoGAPI Tool

The greenness of the developed HPTLC method was evaluated using the Modified Green Analytical Procedure Index (MoGAPI). This tool provides a multi-criteria assessment of an analytical method's environmental impact [22]. While the specific numerical score from this study is not provided in the search results, the use of HPTLC inherently contributes to greenness due to its low solvent consumption per sample and minimal energy requirements compared to techniques like HPLC [22] [34].

The use of ammonia, while effective for achieving separation, is noted in other studies as a non-eco-friendly solvent [7]. The MoGAPI assessment balances such factors against the method's overall advantages, including its stability-indicating capability and the avoidance of extensive sample preparation, which also reduces waste [22].

Greenness Assessment Logic: This diagram outlines the logical framework for evaluating the method's environmental impact using the MoGAPI tool, weighing inherent green strengths against factors like solvent toxicity.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Reagents for Method Implementation

Item	Function / Purpose in the Analysis	Specification / Notes
HPTLC Silica Gel 60 F₂₅₄ Plates	The stationary phase for chromatographic separation.	Aluminum-backed, 10x10 cm, 0.20 mm thickness. Pre-washing with methanol and activation at 110°C is recommended [22].
Chloroform	Component of the mobile phase. Contributes to the solubility and migration of the analytes.	Analytical grade. A solvent of concern in green chemistry; handling should be in a fume hood [22] [7].
Toluene	Component of the mobile phase. Helps in the resolution of the drug bands.	Analytical grade [22].
Methanol	Component of the mobile phase and primary solvent for preparing standard and sample solutions.	Chromatographic grade. Acts as a solubilizing agent [22].
Ammonia Solution	Component of the mobile phase. Acts as a modifier to control pH and improve peak shape.	Analytical grade. Its use is flagged by some greenness assessment tools [22] [7].
Camag Linomat 5	Automated sample applicator for precise and reproducible band application on the HPTLC plate.	Uses a 100 µL syringe; application rate set at 150 nL/sec [22].
Camag TLC Scanner 3	Densitometer for scanning the developed HPTLC plate and quantifying the analyte bands.	Equipped with a deuterium lamp; operated at 224 nm [22].
winCATS Software	Software for instrument control, data acquisition, and processing (e.g., peak integration, calibration curves).	Version 1.3.0 or similar [22].

Discussion

The developed HPTLC method successfully achieves the simultaneous quantification of DAPA and BSF in a combined dosage form. The chromatographic conditions resulted in well-resolved peaks with Rf values of 0.22 for DAPA and 0.63 for BSF, with no interference from excipients or degradation products [22].

The forced degradation studies revealed a critical insight into the stability profile of the drug combination: DAPA was significantly more susceptible to acidic and oxidative hydrolysis compared to BSF. This information is vital for pharmaceutical manufacturers in designing stable formulations and establishing appropriate storage conditions [22].

From a green chemistry perspective, the method presents a mixed profile. The HPTLC technique itself is advantageous, consuming less solvent and energy than HPLC methods. However, the presence of chloroform and ammonia in the mobile phase presents an environmental and safety concern. Future work could focus on optimizing the mobile phase by substituting these solvents with greener alternatives, such as ethanol or ethyl acetate, without compromising the chromatographic performance [7] [10]. The application of the MoGAPI tool provides a structured and visual assessment, justifying the method's use while acknowledging areas for potential improvement, a crucial aspect of modern analytical research [22].

This application note provides a detailed protocol for a stability-indicating HPTLC-densitometry method for the concurrent analysis of dapagliflozin and bisoprolol. The method is validated, precise, accurate, and specific. When framed within a thesis on greenness assessment, this case study serves as a practical example of how modern analytical methods can be developed and critically evaluated not only for their performance but also for their environmental impact. The use of tools like MoGAPI offers a standardized way to quantify and communicate this aspect, pushing the field of pharmaceutical analysis towards more sustainable practices.

Within the framework of green analytical chemistry, High-Performance Thin-Layer Chromatography combined with densitometry (HPTLC-densitometry) has emerged as a sustainable alternative to conventional chromatographic methods. This case study exemplifies the application of an eco-friendly HPTLC-densitometry method for the simultaneous quantification of Florfenicol (FLR), a broad-spectrum antibiotic, and Meloxicam (MEL), a nonsteroidal anti-inflammatory drug, in bovine muscle tissue [14]. The methodology aligns with the principles of green chemistry by minimizing solvent consumption, reducing waste generation, and incorporating comprehensive greenness assessment tools, thereby contributing to safer food surveillance and environmental protection [14].

The simultaneous monitoring of these pharmacologically distinct agents in edible tissues is crucial for public health protection. Veterinary drug residues can accumulate in animal-derived food products, posing potential risks such as allergic reactions and the development of antimicrobial resistance in humans [14]. The European Commission has set maximum residue limits (MRLs) at 200 µg/kg for FLR and 20 µg/kg for MEL in bovine muscle tissue, highlighting the need for reliable, sensitive, and green analytical methods for regulatory compliance [14].

Experimental Design and Workflow

The analytical procedure for determining FLR and MEL in bovine tissue was designed to be straightforward, cost-effective, and environmentally benign. The workflow encompasses sample preparation, chromatographic separation, and detection, with green chemistry principles integrated at every stage.

The following diagram illustrates the complete experimental workflow, from sample collection to data analysis:

Research Reagent Solutions and Essential Materials

The successful implementation of this eco-friendly HPTLC method relies on specific chemical reagents and specialized equipment. The table below details the key materials and their functions within the analytical protocol.

Table 1: Essential Research Reagents and Materials for HPTLC-Densitometry Analysis of FLR and MEL

Item Name	Function / Role in the Experiment	Specifications / Notes
Florfenicol (FLR)	Broad-spectrum antibiotic analyte; substance to be quantified	Purity ≥98%; stock solution at 5000 µg/mL in methanol [14]
Meloxicam (MEL)	NSAID analyte; substance to be quantified	Purity ≥99.95%; stock solution at 1000 µg/mL in methanol [14]
Esomeprazole (ESO)	Internal Standard (IS)	Compensates for potential wavelength fluctuations and application inconsistencies [14]
HPTLC Silica Gel Plates	Stationary phase for chromatographic separation	Aluminum-backed, 20x20 cm, 5 µm particle size, F254 indicator [14]
Mobile Phase Components	Solvent system for analyte separation	Ethyl acetate, Methanol, Triethylamine, Glacial acetic acid [14]
Ethylenediaminetetraacetic Acid (EDTA)	Sample pre-treatment reagent	Added to tissue homogenate to aid extraction [14]

Detailed Methodology and Protocols

Sample Preparation Protocol

Bovine muscle tissue was obtained from a local supplier, with confirmation that the animals had not been administered any pharmaceuticals prior to slaughter [14].

• Homogenization: Two grams of cattle muscle were thoroughly homogenized using a mortar and pestle.
• Spiking: The homogenized tissue was transferred into separate tubes and spiked individually with appropriate quantities of FLR and MEL standard solutions.
• Internal Standard and Additive: To each sample, 0.50 mL of the Esomeprazole (ESO) internal standard solution (1000 µg/mL) and 300 µL of 0.10 N EDTA solution were added [14].
• Extraction and Filtration: The samples were then processed (with details of extraction completed using methanol), and the final solutions were filtered through a 0.45 µm nylon membrane filter before application on the HPTLC plate [14].

HPTLC-Densitometric Conditions

The chromatographic conditions were meticulously optimized to achieve baseline separation of the target analytes with high sensitivity and minimal environmental impact.

• Stationary Phase: Aluminum HPTLC plates pre-coated with 5 µm silica gel 60 F254.
• Sample Application: Standards and samples were applied as 4 mm bands using an automated applicator (Camag Linomat IV or V) with a syringe, at a constant application rate of 150 nL/s.
• Mobile Phase: A mixture of ethyl acetate, methanol, triethylamine, and glacial acetic acid in the ratio of 9.00 : 1.00 : 0.10 : 0.05 (v/v) was used [14].
• Chromatographic Development: The plates were developed in a twin-trough glass chamber previously saturated with the mobile phase vapor for 15 minutes at room temperature. The ascending development distance was 80 mm.
• Detection and Quantification: After development and drying, the plates were scanned with a TLC scanner (Camag TLC Scanner 3) in the absorbance/reflectance mode at a wavelength of 230 nm. The peak areas of FLR and MEL were measured and normalized against the peak area of the internal standard (Esomeprazole) to generate the calibration curves [14].

Method Validation and Data Analysis

The developed HPTLC-densitometry method was rigorously validated according to the International Conference on Harmonization (ICH) guidelines to ensure its reliability, accuracy, and precision for the intended application [14].

Calibration Data and Validation Parameters

The method demonstrated excellent linearity over the specified concentration ranges for both analytes. The validation parameters confirmed that the method is suitable for the simultaneous quantification of FLR and MEL in spiked bovine tissue.

Table 2: Method Validation Parameters for the HPTLC-Densitometry Analysis of FLR and MEL

Validation Parameter	Florfenicol (FLR)	Meloxicam (MEL)
Linearity Range	0.50 – 9.00 µg/band	0.03 – 3.00 µg/band
Regression Equation	Y = aX + b	Y = aX + b
Correlation Coefficient (R²)	>0.999	>0.999
Accuracy (Recovery %)	Validated per ICH guidelines	Validated per ICH guidelines
Precision (% RSD)	Meets ICH criteria	Meets ICH criteria
Quality Control (QC) Levels	Low: 2.00 µg/bandMedium: 4.00 µg/bandHigh: 7.00 µg/band	Low: 0.30 µg/bandMedium: 1.50 µg/bandHigh: 2.40 µg/band

The validation process included assessing accuracy (recovery), precision (repeatability and intermediate precision), specificity (confirming no interference from excipients or tissue components), and robustness, all of which fell within acceptable limits as per ICH guidelines [14].

Greenness Assessment

The environmental impact of the analytical method was critically evaluated using multiple greenness assessment tools, confirming its eco-friendly profile. This multi-faceted evaluation is a cornerstone of modern green analytical chemistry.

• Analytical Eco-Scale: This semi-quantitative tool penalizes the method for hazardous reagents, energy consumption, and waste generation. A high score (closer to 100) indicates an excellent green method. Similar green HPTLC methods reported scores above 90, signifying minimal environmental impact [8] [11].
• GAPI (Green Analytical Procedure Index): This tool provides a visual profile with pentagrams, evaluating the greenness of each step of the analytical process, from sample collection to final determination.
• AGREE (Analytical GREENness) Calculator: A more recent software-based tool that considers the 12 principles of green analytical chemistry, providing an overall score between 0 and 1. A score of 0.89 was reported for a similar green RP-HPTLC method, indicating high environmental friendliness [8].

The use of ethanol-water mixtures or other optimized mobile phases with lower toxicity, combined with minimal solvent consumption and waste production inherent to the HPTLC technique, contributes significantly to the high greenness scores of this methodology [8] [10].

Application in Food Safety and Discussion

The primary application of this validated method is the monitoring of veterinary drug residues in edible bovine tissue to ensure compliance with regulatory MRLs and protect consumer health [14]. The method successfully addresses the challenge of simultaneously quantifying two drugs from different classes in a complex matrix.

The greenness profile of this HPTLC method presents a significant advantage over traditional HPLC methods, which typically consume larger volumes of solvents and generate more waste [35]. The off-line nature of HPTLC allows for the analysis of multiple samples on a single plate, drastically reducing analysis time, cost, and environmental footprint per sample [35].

Future perspectives in this field point towards the integration of even more sustainable practices. This includes the further development of Reversed-Phase HPTLC (RP-HPTLC) using water-ethanol mobile phases [8] [10], and the incorporation of smartphone-based detection as a portable, cost-effective alternative to commercial densitometers, making quality control testing more accessible [36] [37].

Within the paradigm of green analytical chemistry, high-performance thin-layer chromatography-densitometry (HPTLC-densitometry) is increasingly recognized for its inherent miniaturized nature and reduced solvent consumption compared to conventional separation techniques. The drive towards sustainable laboratory practices has intensified the focus on miniaturized sample preparation and analysis techniques that significantly reduce solvent and sample volumes. This application note details practical protocols and assessments for implementing waste-reducing HPTLC-densitometry methods, contextualized within greenness assessment research for drug development and environmental analysis. The principles of Green Analytical Chemistry (GAC) emphasize reducing hazardous chemical use, minimizing waste generation, and improving safety for analysts and the environment [38] [39]. Miniaturized liquid-phase extraction techniques and analytical-scale separations align with these goals through reduced solvent consumption, decreased waste production, and lower energy requirements [38] [40]. HPTLC-densitometry exemplifies these advantages by utilizing minimal solvent volumes for mobile phase preparation while enabling parallel sample processing, thereby reducing analysis time and resource utilization per sample [4] [41].

Miniaturization Approaches in Analytical Chemistry

Miniaturized Liquid Chromatography Systems

The evolution of liquid chromatography has progressively trended toward miniaturization, with systems now categorized into three primary flow regimes: analytical flow (100-500 μL/min), micro-flow (10-100 μL/min), and nano-flow (<1 μL/min) [42]. This systematic reduction in scale offers substantial benefits for solvent and sample conservation. Miniaturized liquid chromatography (M-LC) systems operate with flow rates in the nano- to microliter per minute range, achieving solvent consumption reductions of up to 1000-fold compared to traditional LC systems [40]. The transition from conventional high-performance liquid chromatography (HPLC) to ultra-high-performance liquid chromatography (UHPLC) with sub-2 µm particles already represented progress, but further miniaturization to capillary and nano-LC systems delivers additional dramatic reductions in solvent use and waste generation [40].

Micro-LC systems provide an optimal balance between the sensitivity gains of nano-flow systems and the operational robustness of analytical-scale platforms, making them particularly suitable for wide-target small-molecule analysis [42]. The sensitivity enhancement achieved through miniaturization stems from reduced on-column sample dilution and improved ionization efficiency, especially when coupled with mass spectrometry [42]. This enables researchers to obtain high-quality analytical data while consuming minimal quantities of precious samples and solvents.

HPTLC-Densitometry as a Miniaturized Platform

HPTLC-densitometry embodies miniaturization principles through its minimal solvent consumption and ability to analyze multiple samples simultaneously on a single plate. The technique typically requires only 10-20 mL of mobile phase for development, regardless of the number of samples applied to the plate (up to 20 or more samples can be processed in parallel) [4] [41]. This multi-sample capability drastically reduces solvent consumption per sample analyzed compared to sequential techniques like HPLC.

The environmental advantages of HPTLC are further enhanced when green solvent systems replace traditional hazardous organic mixtures. Methods utilizing ethanol-water combinations [39] or other low-toxicity solvent systems [4] have demonstrated that ecological considerations need not compromise analytical performance. These approaches align with all twelve principles of green chemistry by minimizing environmental impact while maintaining method robustness and reliability.

Quantitative Assessment of Solvent and Sample Reduction

Solvent Consumption Comparisons Across Techniques

Table 1: Solvent Consumption Comparison Between Analytical Techniques

Analytical Technique	Typical Solvent Volume per Analysis	Estimated Waste Generation	Key Greenness Advantages
Conventional HPLC	500-1000 mL/day	High	Baseline reference
UPLC	100-300 mL/day	Moderate	5-10x reduction vs. HPLC
Micro-LC	1-10 mL/day	Low	~100x reduction vs. HPLC [40]
Nano-LC	0.001-0.1 mL/day	Very Low	~1000x reduction vs. HPLC [40]
HPTLC-Densitometry	10-20 mL/plate (10-20 samples)	Very Low	Parallel processing; <1 mL/sample [4]

Greenness Assessment Metrics for Method Evaluation

Table 2: Greenness Assessment Scores of Reported HPTLC Methods

Analytical Method	Analyzed Compounds	Mobile Phase Composition	Greenness Assessment Tools	Assessment Results
RP-HPTLC [39]	Apremilast	Ethanol/Water (65:35, v/v)	Analytical Eco-Scale, ChlorTox, AGREE	AES: 93/100, ChlorTox: 0.66 g, AGREE: 0.89/1.0
HPTLC-Densitometry [4]	Aspirin, Metoclopramide	Cyclo-hexane/Methanol/Methylene Chloride (1:4:1, v/v/v)	Analytical Eco-Scale, GAPI, AGREE	Excellent green spectrophotometric method; Acceptable green HPTLC method
HPTLC with UV/Fluorescence [41]	Ivabradine, Metoprolol	Chloroform/Methanol/Formic Acid/Ammonia (8.5:1.5:0.2:0.1, v/v)	Analytical Eco-Scale, GAPI, AGREE	Greenness confirmed by three assessment tools

Experimental Protocols for Green HPTLC-Densitometry

Protocol 1: Method Development with Green Solvent Systems

Principle: This protocol establishes a framework for developing HPTLC-densitometry methods using green solvent systems, specifically ethanol-water combinations, to replace hazardous solvents while maintaining chromatographic performance [39].

Materials and Equipment:

• HPTLC plates: RP-18 silica gel 60 F254S plates (e.g., Merck)
• Green solvents: Ethanol (HPLC grade), purified water
• Application device: CAMAG Linomat IV or V automatic applicator
• Development chamber: CAMAG twin-trough glass chamber
• Densitometer: CAMAG TLC Scanner 3 with winCATS software
• UV lamp for visualization (if needed)

Procedure:

• Standard Solution Preparation: Prepare stock solutions of analytes in methanol or ethanol at 1 mg/mL. Prepare working solutions by appropriate dilution with the same solvent.
• Plate Application: Apply samples as bands (4-6 mm width) using the automatic applicator. Maintain a minimum of 10 mm from the bottom edge and 15 mm between bands.
• Mobile Phase Optimization: Test ethanol-water combinations in ratios from 50:50 to 80:20 (v/v). Adjust ratio to achieve target retardation factor (Rf) of 0.4-0.7.
• Chromatographic Development: Pre-saturate the development chamber with mobile phase for 20 minutes at room temperature. Develop the plate over a migration distance of 70-80 mm.
• Densitometric Analysis: After development and drying, scan plates at the determined λmax for each analyte (typically 230-300 nm depending on the compound).
• Validation: Validate the method according to ICH Q2(R1) guidelines for linearity, precision, accuracy, specificity, LOD, and LOQ.

Greenness Assessment: Evaluate the final method using multiple greenness assessment tools (Analytical Eco-Scale, AGREE, ChlorTox). An Analytical Eco-Scale score above 75 indicates an excellent green method, while scores of 50-75 indicate acceptable greenness [39].

Protocol 2: Smartphone-Assisted HPTLC with Image J Analysis

Principle: This protocol utilizes smartphone imaging coupled with Image J software analysis as an alternative to conventional densitometry, reducing equipment costs and maintaining analytical performance for appropriate applications [43].

Materials and Equipment:

• TLC aluminum plates (20 × 20 cm) pre-coated with silica gel 60 F254
• Mobile phase: Methylene chloride/Methanol/Glacial acetic acid (60:40:2, v/v)
• UV lamp (254 nm) for band visualization
• Smartphone with high-resolution camera
• Computer with Image J software (freely available from NIH)

Procedure:

• Sample Application and Development: Apply samples as bands (6 mm width) using a semi-automatic applicator or micropipette. Develop plates in a mobile phase-saturated chamber to a distance of 80 mm.
• Image Acquisition: After development and drying, visualize bands under a UV lamp at 254 nm. Capture images using a smartphone camera mounted on a stand to maintain consistent distance and angle. Ensure uniform lighting and avoid shadows.
• Image J Analysis:
  • Open the TLC plate image in Image J software
  • Convert the image to 8-bit grayscale (Image > Type > 8-bit)
  • Draw rectangular selections around each band and background areas
  • Use the "Plot Profile" function (Analyze > Plot Profile) to generate intensity plots
  • Measure peak areas for each band using the "Wand" tool or manual selection
  • Subtract background signal from adjacent blank plate areas
• Calibration Curve Construction: Plot the measured peak areas against analyte concentrations to generate calibration curves. Determine sample concentrations from the regression equation.

Method Validation: Validate the smartphone-Image J method by comparing results with conventional HPTLC-densitometry for accuracy and precision. Statistical comparison (t-test, F-test) should show no significant difference between the methods at 95% confidence level [43].

Greenness Assessment Workflow

The following diagram illustrates the systematic workflow for developing and evaluating green HPTLC-densitometry methods:

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Materials for Green HPTLC-Densitometry

Item	Specification	Function	Green Alternatives
HPTLC Plates	Silica gel 60 F254, RP-18, CN-modified	Stationary phase for separation	Standard silica (most eco-friendly)
Solvent Systems	Ethanol, water, methanol, ethyl acetate	Mobile phase components	Ethanol-water mixtures preferred [39]
Application System	CAMAG Linomat IV/V autosampler	Precise sample application	-
Development Chamber	Twin-trough glass chamber	Controlled mobile phase development	-
Detection System	CAMAG TLC Scanner 3	Densitometric quantification	Smartphone + Image J as alternative [43]
Greenness Assessment Tools	AGREE, Analytical Eco-Scale, GAPI	Quantitative method evaluation	-

The strategic implementation of miniaturization principles in HPTLC-densitometry through green solvent systems, micro-scale approaches, and innovative detection methods significantly advances sustainable analytical practices. The protocols and assessments detailed in this application note provide researchers with practical frameworks for developing analytical methods that align with green chemistry principles while maintaining rigorous performance standards. As the field progresses, integration of these approaches with automated miniaturized sample preparation techniques [38] and continued adoption of greenness assessment metrics will further enhance the environmental profile of analytical laboratories. The quantitative data presented demonstrates that substantial reductions in solvent consumption and waste generation are achievable without compromising analytical quality, offering both ecological and economic benefits for drug development and environmental monitoring applications.

Application in Stability-Indicating Methods and Forced Degradation Studies

High-Performance Thin-Layer Chromatography coupled with densitometric detection (HPTLC-densitometry) has emerged as a powerful analytical technique for pharmaceutical analysis, particularly in the development of stability-indicating methods and forced degradation studies. These applications are critical for determining the intrinsic stability of drug substances and identifying potential degradation pathways, thereby ensuring drug safety and efficacy. The technique provides exceptional versatility for separating active pharmaceutical ingredients from their degradation products while offering advantages of low solvent consumption, high sample throughput, and cost-effectiveness—attributes that align with the principles of green analytical chemistry. This document presents comprehensive application notes and experimental protocols for implementing HPTLC-densitometry in stability-indicating method development, framed within a broader research context assessing the greenness of these analytical approaches.

Fundamental Principles of HPTLC-Densitometry in Stability Testing

HPTLC-densitometry operates on the principle of adsorption chromatography, where the mobile phase moves through the stationary phase via capillary action [44]. Components separate according to their differential affinities toward the adsorbent, with those having more affinity toward the stationary phase traveling slower than those with lesser affinity [44]. In stability-indicating applications, this separation mechanism allows for the effective resolution of drug substances from their degradation products, which is essential for accurate quantification and stability assessment.

The densitometric detection component provides quantitative capabilities through reflectance/absorbance measurements at selected wavelengths [44]. The scanner converts bands on the chromatographic layer into peak profiles similar to HPLC, where peak height or area correlates with the concentration of the substance [44]. This detection approach enables precise quantification of both the parent drug and its degradation products when properly resolved. The stability-indicating nature of the method is confirmed when it can effectively separate the drug from its degradation products and accurately quantify the drug substance in the presence of these impurities [45].

Experimental Protocols

Method Development and Optimization Protocol

Objective: To develop and optimize a validated HPTLC-densitometry method for stability-indicating analysis of drug substances.

Materials and Reagents:

• HPTLC plates (silica gel 60 F254, 20 × 10 cm or 20 × 20 cm)
• Analytical standard of drug substance
• Pharmaceutical formulation(s)
• HPLC-grade solvents for mobile phase preparation
• Volumetric flasks, micropipettes, and sample vials
• Amber glassware for light-sensitive compounds

Procedure:

• Stationary Phase Selection:

  • Select appropriate pre-coated HPTLC plates based on drug polarity
  • Common choices include silica gel 60 F254, alumina, or chemically modified plates
  • Pre-wash plates with methanol if necessary to remove impurities [44]
  • Activate plates at 110-120°C for 30 minutes prior to sample application if exposed to humidity [44]

• Mobile Phase Optimization:

  • Test different solvent systems based on drug polarity using trial-and-error approach [44]
  • Adjust proportions to achieve optimal resolution (Rf value between 0.2-0.8)
  • Incorporate modifiers like ammonia or acetic acid to improve spot characteristics
  • Example systems from literature:
    • n-propanol-ethanol-6M ammonia (4:1:2, v/v/v) for moxifloxacin [45]
    • Methanol-toluene-triethylamine (6:4:0.1, v/v/v) for vonoprazan fumarate [46]
    • n-hexane-ethyl acetate (42.5:7.5, v/v) for nandrolone decanoate [29]
    • Ethyl acetate-toluene-triethylamine-acetic acid (4:4:2:0.4, v/v/v/v) for bupivacaine and meloxicam [47]

• Sample Preparation:

  • Prepare standard stock solution (1 mg/mL) in appropriate solvent
  • Prepare sample solutions from pharmaceutical formulations by extraction/filtration
  • For degradation studies, prepare stressed samples as described in Section 3.2

• Chromatographic Conditions:

  • Apply samples as bands (4-6 mm width) using automatic applicator
  • Application volume typically 1-5 μL for quantitative analysis [44]
  • Develop plates in saturated twin-trough chamber (saturation time: 20-30 minutes)
  • Development distance: 7-8 cm at room temperature
  • Dry plates thoroughly after development

• Detection and Scanning:

  • Scan plates in reflectance/absorbance mode at selected wavelength
  • Select wavelength based on maximum absorbance of drug substance
  • Example detection wavelengths:
    • 298 nm for moxifloxacin [45]
    • 267 nm for vonoprazan fumarate [46]
    • 245 nm for nandrolone decanoate [29]
    • 260 nm for bupivacaine and meloxicam [47]
  • Use deuterium or tungsten lamp with slit dimensions 5 × 0.2 mm or 3 × 0.45 mm
  • Scanning speed: 20 mm/s [48]

Forced Degradation Studies Protocol

Objective: To subject drug substances to various stress conditions and separate degradation products using the developed HPTLC-densitometry method.

Materials:

• Drug substance in pure form
• Acid (e.g., 0.1-5 M HCl)
• Base (e.g., 0.1-5 M NaOH)
• Oxidizing agent (e.g., 3-30% H₂O₂)
• Thermal stability chamber
• Photostability chamber
• Amber and clear glass vials

Procedure:

• Acidic Degradation:

  • Prepare drug solution in 0.1-2 M HCl
  • Heat at 60-80°C for specified duration (1-24 hours)
  • Neutralize with base after degradation period
  • Analyze by HPTLC-densitometry alongside untreated drug

• Alkaline Degradation:

  • Prepare drug solution in 0.1-2 M NaOH
  • Heat at 60-80°C for specified duration (1-24 hours)
  • Neutralize with acid after degradation period
  • Analyze by HPTLC-densitometry alongside untreated drug

• Oxidative Degradation:

  • Prepare drug solution in 3-15% H₂O₂
  • Keep at room temperature or heated (40-60°C) for specified duration (1-24 hours)
  • Analyze by HPTLC-densitometry alongside untreated drug

• Thermal Degradation:

  • Expose solid drug substance to dry heat (70-105°C) for 1-7 days
  • Prepare solutions of heat-exposed drug
  • Analyze by HPTLC-densitometry alongside untreated drug

• Photolytic Degradation:

  • Expose solid drug substance and/or solutions to UV light (e.g., 1.2 million lux hours)
  • Prepare solutions of light-exposed drug
  • Analyze by HPTLC-densitometry alongside protected control

• Data Interpretation:

  • Compare chromatograms of stressed samples with untreated drug
  • Identify new peaks corresponding to degradation products
  • Confirm resolution of degradation products from main drug peak
  • Calculate percentage degradation under each condition

Method Validation Protocol

Objective: To validate the stability-indicating HPTLC-densitometry method according to International Conference on Harmonisation (ICH) guidelines.

Validation Parameters:

• Specificity:

  • Demonstrate separation of drug from degradation products
  • Confirm no interference from excipients in formulations
  • Verify peak purity using spectral comparison [29]

• Linearity:

  • Prepare standard solutions at 5-6 concentration levels
  • Analyze in triplicate and plot peak area vs concentration
  • Calculate correlation coefficient, slope, and intercept
  • Acceptable correlation coefficient: ≥0.999 [29]

• Precision:

  • Repeatability: Analyze six replicates of same concentration
  • Intermediate precision: Analyze on different days, different analysts
  • Acceptable relative standard deviation (RSD): ≤2% [29]

• Accuracy:

  • Perform recovery studies by standard addition method
  • Spike pre-analyzed samples with known amounts of standard
  • Calculate percentage recovery (acceptable range: 98-102%)

• Robustness:

  • Deliberately vary method parameters (mobile phase composition, development distance, saturation time)
  • Evaluate impact on Rf values and resolution

• Limit of Detection (LOD) and Limit of Quantification (LOQ):

  • Calculate based on standard deviation of response and slope of calibration curve
  • Typical LOD and LOQ values in ng/spot range [45] [29]

Table 1: Exemplary Validation Parameters from HPTLC-Densitometry Stability-Indicating Methods

Drug Substance	Linear Range	Correlation Coefficient (r)	LOD	LOQ	Precision (% RSD)	Reference
Moxifloxacin	100-800 ng/spot	0.9925	3.90 ng/spot	11.83 ng/spot	<2%	[45]
Vonoprazan fumarate	200-1200 ng/band	0.9996	-	-	-	[46]
Nandrolone decanoate	0.780-12.500 μg/spot	0.9998	0.231 μg/spot	0.700 μg/spot	<2%	[29]
Citicoline	0.05-1.25 μg/band	-	-	0.043 μg/band	-	[49]
Tyrosine	0.12-2.5 μg/band	-	-	0.107 μg/band	-	[49]

Applications in Pharmaceutical Analysis

Stability-Indicating Method for Moxifloxacin

A validated stability-indicating HPTLC-densitometry method was developed for moxifloxacin in bulk drug and pharmaceutical formulations [45]. The method employed TLC aluminium plates pre-coated with silica gel 60F-254 with n-propanol-ethanol-6M ammonia solution (4:1:2, v/v/v) as mobile phase. Compact spots were obtained at Rf 0.58 ± 0.02 with linear response in the range of 100-800 ng/spot (r=0.9925). The method effectively separated moxifloxacin from its degradation products formed under various stress conditions (acidic, alkaline, oxidative, thermal, and photolytic). The method was also utilized to investigate degradation kinetics, with Arrhenius plots constructed to calculate apparent pseudo-first-order rate constant, half-life, and activation energy [45].

Stability Study of Vonoprazan Fumarate

A stability-indicating HPTLC-densitometry method was developed for vonoprazan fumarate, with a compact band at Rf value of 0.43 ± 0.1 [46]. The method showed good linear relationship (R² = 0.9996) between peak area and concentration in the range 200-1200 ng/band. Forced degradation studies revealed degradation under alkaline conditions following first-order kinetics. The method was successfully applied for estimation of the drug in synthetic mixture, demonstrating its applicability in quality control and stability testing [46].

Analysis of Nandrolone Decanoate in Injection Formulation

An HPTLC-densitometry method was developed for determination of nandrolone decanoate in commercial injection formulation [29]. The method employed CN modified silica gel 60F254 plates with n-hexane-ethyl acetate (42.5:7.5, v/v) as mobile phase, detecting at 245 nm. The method showed good precision (CV < 2%) and accuracy close to 100.3%, with LOD and LOQ of 0.231 and 0.700 μg/spot, respectively. The analysis of pharmaceutical formulation indicated drug content of 50.5 mg/mL (101.0% of label claim), demonstrating the method's suitability for routine quality control [29].

Table 2: Chromatographic Conditions for Various Drug Substances Using HPTLC-Densitometry

Drug Substance	Stationary Phase	Mobile Phase Composition	Detection Wavelength (nm)	Rf Value	Reference
Moxifloxacin	Silica gel 60F-254	n-propanol-ethanol-6M ammonia (4:1:2, v/v/v)	298	0.58 ± 0.02	[45]
Vonoprazan fumarate	Silica gel 60 F254	Methanol:toluene:triethylamine (6:4:0.1, v/v/v)	267	0.43 ± 0.1	[46]
Nandrolone decanoate	CN silica gel 60F254	n-hexane-ethyl acetate (42.5:7.5, v/v)	245	0.57 ± 0.02	[29]
Bupivacaine and meloxicam	Silica gel 60 F254	Ethyl acetate/toluene/triethylamine/acetic acid (4:4:2:0.4, v/v/v/v)	260	-	[47]
Citicoline and tyrosine	Silica gel 60 F254	Phosphate buffer pH=4:methanol (70:30, v/v)	256	-	[49]
Safinamide mesylate	Silica gel 60 F254	Ethyl acetate:methanol:aqueous ammonium hydroxide (9.0:1.2:0.1, v/v/v)	210	-	[48]

Simultaneous Determination of Drug Combinations

HPTLC-densitometry has been successfully applied to simultaneous determination of drug combinations in presence of their degradation products. A method was developed for concurrent determination of bupivacaine and meloxicam in their co-formulated mixture, separating them from their possible degradation products [47]. The method used silica gel 60 F254 as stationary phase and ethyl acetate/toluene/triethylamine/acetic acid (4:4:2:0.4, v/v/v/v) as developing system with detection at 260 nm. Another method was developed for simultaneous quantification of citicoline and tyrosine using a green HPTLC system composed of phosphate buffer pH=4 and methanol (70:30 v/v) on silica gel TLC plates (G60 F254) with UV detection at 256 nm [49].

Kinetic Studies of Drug Degradation

HPTLC-densitometry has been effectively utilized to investigate degradation kinetics of pharmaceutical compounds. The kinetic study of moxifloxacin degradation under acidic and alkaline conditions at different temperatures enabled construction of Arrhenius plots and calculation of kinetic parameters including apparent pseudo-first-order rate constant, half-life, and activation energy [45]. Similarly, the alkaline degradation kinetic study of vonoprazan fumarate revealed first-order degradation kinetics [46]. These kinetic studies provide valuable information for predicting drug stability and establishing appropriate storage conditions.

The following workflow diagram illustrates the comprehensive process for developing and applying HPTLC-densitometry in stability-indicating methods and forced degradation studies:

HPTLC Stability Study Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for HPTLC-Densitometry Stability Studies

Item	Specification/Example	Function/Purpose	Reference
HPTLC Plates	Silica gel 60 F254 on aluminum/glass sheets (20×10 cm or 20×20 cm)	Stationary phase for chromatographic separation	[45] [46] [29]
Mobile Phase Solvents	n-propanol, ethanol, methanol, ethyl acetate, n-hexane, toluene	Mobile phase components for optimal separation	[45] [46] [29]
Modifiers	Triethylamine, ammonia solution, acetic acid	Improve peak shape and resolution	[46] [47] [48]
Sample Applicator	Automatic applicator (e.g., CAMAG Linomat 5)	Precise application of samples as bands	[47] [49] [48]
Development Chamber	Twin-trough glass chamber	Controlled mobile phase development	[29] [44]
Densitometer Scanner	TLC scanner with UV/Vis deuterium and tungsten lamps	Quantitative detection of separated compounds	[29] [47] [49]
Forced Degradation Reagents	HCl, NaOH, H₂O₂	Induce degradation under stress conditions	[45] [46] [47]
Reference Standards	Certified drug substance standards (purity >99%)	Method development, calibration, and quantification	[29] [49] [48]

Greenness Assessment in HPTLC-Densitometry Methods

The greenness of HPTLC-densitometry methods can be evaluated using multiple assessment tools including the Green Analytical Procedure Index (GAPI), AGREE metrics, and the newer Blue Analytical Greenness Index (BAGI) and RGB12 models [48] [50]. HPTLC-densitometry inherently offers several green advantages compared to other chromatographic techniques:

• Reduced Solvent Consumption: Minimal mobile phase volume required for development
• Minimal Sample Preparation: Reduced need for extensive sample pretreatment
• Energy Efficiency: Lower energy requirements compared to HPLC systems
• Reduced Waste Generation: Minimal organic solvent waste

Studies have directly compared the greenness profiles of HPTLC methods with other techniques. For example, in the analysis of levocloperastine and chlorpheniramine, the HPTLC method showed comparable sustainability to spectrophotometric methods, though it was less green than the spectrophotometric approach [50]. The environmental impact of HPTLC methods can be further reduced by selecting greener solvent systems, such as ethanol-water or methanol-water mixtures, instead of more toxic solvents [49].

HPTLC-densitometry represents a robust, versatile, and environmentally favorable analytical technique for stability-indicating method development and forced degradation studies. The method provides adequate specificity to separate drugs from their degradation products, sensitivity for quantification at low concentration levels, and precision for reliable stability assessment. The capability for simultaneous analysis of multiple samples significantly enhances throughput compared to sequential techniques like HPLC. When developed with green chemistry principles in mind, HPTLC-densitometry methods can offer sustainable solutions for pharmaceutical analysis while maintaining the rigorous analytical performance required for regulatory compliance. The continued development and application of these methods, coupled with comprehensive greenness assessment using modern metrics, will further establish HPTLC-densitometry as a valuable tool in stability testing and pharmaceutical quality control.

Optimizing and Troubleshooting Green HPTLC-Densitometry Methods

Common Challenges in Method Development and Paths to Resolution

High-Performance Thin-Layer Chromatography coupled with densitometry (HPTLC-densitometry) is an advanced analytical technique that has evolved from a simple qualitative tool into a versatile platform for the quantitative analysis of complex mixtures. Its inherent advantages, including high sample throughput, minimal solvent consumption, and cost-effectiveness, make it particularly suitable for pharmaceutical analysis [51]. The alignment of HPTLC with Green Analytical Chemistry (GAC) principles has further increased its appeal for modern laboratories seeking to reduce their environmental footprint [52]. However, the development of robust, validated HPTLC-densitometry methods presents several technical challenges that require systematic resolution. This application note details these common obstacles and provides evidence-based paths to their resolution, framed within the context of sustainable method development.

Common Challenges and Resolutions in HPTLC-Densitometry

Challenge 1: Achieving Optimal Separation in Complex Matrices

Description: A primary challenge in HPTLC method development is achieving baseline separation of analytes from each other and from matrix components, which is crucial for accurate quantification. This is particularly difficult for compounds with similar chemical structures or in complex biological and pharmaceutical matrices where excipients and endogenous compounds can interfere [53].

Paths to Resolution:

• Systematic Mobile Phase Optimization: A trial-and-error approach testing solvent systems of varying polarities and pH often succeeds. For a novel combined formulation of empagliflozin and metoprolol, researchers achieved excellent separation (Rf 0.54 and 0.26, respectively) using chloroform: methanol: toluene (3.0:2.5:4.5, v/v/v) [54].
• pH Modification: Adding small amounts of acids, bases, or buffers can dramatically improve separation. For vonoprazan fumarate and aspirin, the addition of glacial acetic acid (2% of mobile phase volume) to methylene chloride and methanol (60:40) significantly enhanced spot shape and resolution [24].
• Multi-Modal Development: Employing both normal-phase (NP) and reversed-phase (RP) HPTLC can provide complementary separation mechanisms. A study on sorafenib developed both RP (isopropanol: water: glacial acetic acid) and NP (n-butanol: ethyl acetate) methods, offering flexibility for different analytical needs [52].

Table 1: Mobile Phase Optimization for Specific Separations

Analytes	Matrix	Optimized Mobile Phase	Resulting Rf Values
Empagliflozin & Metoprolol [54]	Bilayer Tablet	Chloroform: Methanol: Toluene (3.0:2.5:4.5, v/v/v)	0.54 & 0.26
Vonoprazan & Aspirin [24]	Pharmaceutical Dosage Form	Methylene Chloride: Methanol: Glacial Acetic Acid (60:40:2, v/v)	0.45 & 0.75
Apixaban, Edoxaban, Rivaroxaban & Rosuvastatin [53]	Human Plasma	Toluene–Ethyl Acetate–Methanol–25% Ammonia (3.5:4.5:2:0.2, V/V)	Well-resolved
Sorafenib (NP-HPTLC) [52]	Bulk & Formulations	n-butanol : ethyl acetate	0.7 ± 0.2

Challenge 2: Detection Sensitivity and Specificity

Description: Detecting and accurately quantifying analytes at low concentrations, especially in the presence of interfering substances, is a significant hurdle. This is critical for impurity profiling, analysis in biological fluids, and ensuring method robustness.

Paths to Resolution:

• Wavelength Selection and Scanning: Determining the λmax of each analyte by post-separation UV-Vis spectrum scanning ensures optimal detection sensitivity. For caffeine analysis in saliva, the wavelength was shifted from 254 nm to the λmax of 275 nm, improving detection and quantification limits to 2.42 and 7.34 ng/band, respectively [55].
• Advanced Detection Modalities: Hyphenation with spectroscopic techniques overcomes specificity limitations.
  • HPTLC-MS: Combines separation with structural identification, ideal for unknown impurity characterization [51].
  • HPTLC-SERS: Uses Surface-Enhanced Raman Spectroscopy for molecular fingerprinting of compounds with low native fluorescence [51].
  • Laser-Induced Fluorescence (LIF): Offers superior sensitivity for native fluorescent compounds or those forming fluorescent derivatives. A LIF-based method for thioridazine provided high sensitivity and additional selectivity via fluorescence lifetime analysis [56].
• Internal Standardization: Using an internal standard (e.g., Esomeprazole) corrects for procedural errors and instrumental fluctuations, improving accuracy and precision, especially in bioanalysis [14].

Challenge 3: Method Validation and Regulatory Compliance

Description: For a method to be suitable for quality control or regulatory submission, it must be rigorously validated as per guidelines (e.g., ICH Q2(R2), US FDA) to prove it is reliable, reproducible, and fit-for-purpose.

Paths to Resolution:

• Comprehensive Validation Protocols: Adhere strictly to ICH guidelines, assessing parameters such as linearity, accuracy, precision, specificity, LOD, LOQ, and robustness. A method for empagliflozin and metoprolol demonstrated linearity (100-600 ng/band and 500-3000 ng/band), high accuracy (recoveries 99.54-101.21% and 98.23-100.73%), and precision (%RSD < 2%) [54].
• Forced Degradation Studies: To establish the stability-indicating nature of the method, stress samples (acid, base, oxidative, thermal, photolytic) should be analyzed. The method should effectively resolve the analytes from their degradation products, confirming specificity [54].
• Robustness Testing: Deliberately introducing small, deliberate variations in method parameters (e.g., mobile phase composition ±2%, saturation time ±5 min) ensures the method's reliability. The salivary caffeine method showed minimal impact on Rf values under such variations [55].

Table 2: Typical Validation Parameters and Target Acceptance Criteria

Validation Parameter	Target Acceptance Criteria	Exemplary Data from Literature
Linearity	Correlation coefficient (R²) ≥ 0.99	R² = 0.9998 for Sorafenib (200-1000 ng/spot) [52]
Accuracy	Recovery 98-102%	98.23-101.21% for Empagliflozin & Metoprolol [54]
Precision (Repeatability)	%RSD ≤ 2%	%RSD 1.82 for salivary caffeine [55]
LOD/LOQ	Sufficient for intended use	LOD/LOQ: 24.67/74.76 ng/band (Empagliflozin) [54]
Robustness	Insignificant impact on results	RF stable with mobile phase variations [55]

Challenge 4: Integrating Greenness and Sustainability

Description: Modern analytical development must address the environmental impact of methods, which involves reducing hazardous solvent consumption, waste generation, and energy usage.

Paths to Resolution:

• Solvent Selection and Miniaturization: Prioritize less toxic solvents (e.g., ethanol, ethyl acetate, isopropanol) and minimize total volume. HPTLC is inherently green, consuming only ~10-15 mL of mobile phase for simultaneous analysis of up to 20 samples [51] [52].
• Quantitative Greenness Assessment: Use standardized metrics to evaluate and communicate environmental performance.
  • AGREE (Analytical GREEnness Metric): Provides a comprehensive 0-1 score. Methods for sorafenib achieved high AGREE scores of 0.82-0.83 [52].
  • Other Tools: NEMI, GAPI, and ComplexGAPI provide complementary assessments, with the HPTLC method for bisoprolol, amlodipine, and its impurity achieving perfect scores on some metrics [6].
• Innovative Low-Cost Alternatives: Smartphone-based detection, using built-in cameras and image analysis software like ImageJ, offers a cost-effective, portable, and sustainable alternative to conventional densitometry scanners without sacrificing accuracy [24].

Detailed Experimental Protocol: A Representative Green HPTLC-Densitometry Method

The following protocol, based on the development of a stability-indicating method for a novel combined formulation of Empagliflozin (EMP) and Metoprolol Succinate (METO) [54], serves as a template for robust method development.

The Scientist's Toolkit: Essential Materials and Reagents

Table 3: Key Research Reagent Solutions and Materials

Item	Specification / Function
HPTLC Plates	Silica gel 60 F₂₅₄, aluminum-backed, 20x10 cm (e.g., Merck). The F₂₅₄ indicator allows for UV visualization at 254 nm.
Sample Applicator	Automated applicator (e.g., CAMAG Linomat IV/V) with a 100-μL syringe for precise, band-wise application.
Chromatography Chamber	Twin-trough glass chamber for ascending development with pre-saturation to ensure equilibrium.
Mobile Phase	Chloroform: Methanol: Toluene (3.0:2.5:4.5, v/v/v). Prepared in a measuring cylinder and mixed thoroughly. Hood use is recommended.
Densitometer	TLC Scanner (e.g., CAMAG TLC Scanner 3) with a deuterium lamp, controlled by software (e.g., WinCATS).
Validation Standards	Certified reference standards of EMP and METO with purity >98% for preparing calibration solutions.

Step-by-Step Procedure

• Standard Solution Preparation: Prepare individual stock solutions of EMP and METO in methanol at a concentration of 1 mg/mL. Prepare working standard mixtures by appropriate dilution to obtain a calibration range of 100-600 ng/band for EMP and 500-3000 ng/band for METO.

• Sample Preparation: For the bilayer tablet, weigh and powder not less than 20 tablets. Accurately weigh a portion of the powder equivalent to one tablet and extract with methanol via sonication for 15-30 minutes. Filter the solution and dilute appropriately to fall within the calibration range.

• Plate Prewashing & Pre-conditioning: Pre-wash the HPTLC plates with methanol and dry in an oven at 100°C for 5 minutes to remove any contaminants. Allow to cool in a desiccator before application.

• Sample Application: Using the automatic applicator, apply bands of standard and sample solutions (e.g., 6 mm band width) onto the HPTLC plate, 10 mm from the bottom and 5 mm apart from each other. The application volume is typically 10-20 μL.

• Chromatographic Development: Pour the mobile phase into one trough of the pre-saturated twin-trough chamber. Place the spotted plate in the other trough and allow to saturate for 15-20 minutes. Then, develop the plate in the ascending mode to a distance of 80 mm from the point of application.

• Drying and Visualization: After development, remove the plate and air-dry thoroughly in a fume hood to completely evaporate the solvents.

• Densitometric Scanning: Scan the developed, dried plate using the TLC scanner in absorbance-reflection mode at 222 nm. Use a deuterium lamp, a slit dimension of 6.00 x 0.30 mm, and a scanning speed of 20 mm/s.

• Data Analysis: Generate calibration curves by plotting the peak area against the corresponding concentration for each analyte. Use the regression equations to determine the concentration of EMP and METO in the sample solutions.

HPTLC Method Development Workflow

The development of a robust HPTLC-densitometry method is a multi-faceted process that systematically addresses challenges related to separation, detection, validation, and sustainability. By leveraging strategic mobile phase optimization, advanced detection modalities, rigorous validation protocols, and quantitative greenness assessment tools, analysts can overcome these hurdles. The resulting methods are not only scientifically sound and compliant with regulatory standards but also align with the principles of Green Analytical Chemistry, offering efficient, cost-effective, and environmentally responsible solutions for pharmaceutical analysis and quality control.

Optimizing Chamber Saturation and Mobile Phase Composition for Efficiency

Within the framework of green analytical chemistry, the optimization of High-Performance Thin-Layer Chromatography-densitometry (HPTLC-densitometry) methods is paramount for developing sustainable quality control protocols in pharmaceutical and food safety analysis. This application note details validated procedures for optimizing two critical parameters that directly influence method efficiency, reproducibility, and environmental impact: chamber saturation time and mobile phase composition. The protocols are contextualized within a broader thesis on the greenness assessment of HPTLC methods, aligning with principles of reducing organic solvent consumption, minimizing waste, and enhancing analytical throughput [23] [57].

Core Concepts and Signaling Pathways

The optimization process is governed by a systematic workflow where chamber saturation and mobile phase composition are interdependent variables affecting the final chromatographic outcome. The following diagram illustrates the decision pathway for method development.

Key Research Reagent Solutions

The following table catalogues essential materials and reagents commonly employed in the development and analysis of green HPTLC-densitometry methods, as featured in the cited research.

Table 1: Essential Research Reagents and Materials for HPTLC-Densitometry

Item Name	Function / Application	Specific Example from Research
Silica Gel 60 F254 Plates	Stationary phase for chromatographic separation.	Standard plates used for analyzing Rhodamine B, anti-asthmatic drugs, and salivary caffeine [58] [7] [55].
Methanol	Common solvent for sample preparation and mobile phase component.	Used in sample dissolution for vonoprazan/aspirin analysis and in the mobile phase for florfenicol/meloxicam determination [14] [43].
Green Solvent Alternatives	Less hazardous mobile phase components.	Cyclo-hexane used as a greener alternative in the analysis of aspirin and metoclopramide [4].
Ion-Pair Reagents / Surfactants	Mobile phase modifiers to improve separation.	Sodium dodecyl sulphate (SDS) used to modify retention of neurogenerative drugs [59].
Internal Standards	Compounds used to improve quantification accuracy.	Esomeprazole used as an internal standard for the analysis of florfenicol and meloxicam [14].

Experimental Protocols

Protocol 1: Optimization of Chamber Saturation

Principle: A well-saturated developing chamber ensures a uniform solvent vapor phase, which is critical for achieving reproducible Retention factor (Rf) values, sharp band shapes, and high resolution [55]. Inadequate saturation is a major source of irreproducibility in planar chromatography.

Materials:

• HPTLC chamber (e.g., Camag twin-trough glass chamber)
• HPTLC plates
• Prepared mobile phase
• Filter paper (for lining the chamber)

Procedure:

• Preparation: Line one trough of the twin-trough chamber with filter paper. This increases the surface area for evaporation and accelerates vapor phase equilibrium.
• Saturation: Pour a sufficient volume of the mobile phase into the trough containing the filter paper (e.g., 10-20 mL per trough [60] [4]). Close the chamber lid securely.
• Equilibration: Allow the chamber to stand at room temperature for a defined period. Literature shows saturation times typically range from 15 to 30 minutes [14] [43] [4].
• Development: Introduce the spotted HPTLC plate into the second, dry trough of the chamber and close the lid immediately to minimize vapor loss. Proceed with ascending development.

Validation via Robustness Testing: The optimized saturation time should be validated as part of method robustness. Intentionally vary the saturation time (e.g., ± 5 minutes from the standard time) and analyze the impact on the Rf value and peak shape of the target analyte. A robust method will show minimal change. A study on salivary caffeine analysis confirmed method robustness when small changes to saturation time resulted in accuracy values between 98.62% and 104.42% [55].

Protocol 2: Optimization of Mobile Phase Composition

Principle: The mobile phase's chemical composition determines the selectivity and efficiency of separation. The goal is to find the simplest mixture that provides baseline resolution (Rf ≈ 0.5 ± 0.3) for all analytes of interest while prioritizing less hazardous, "green" solvents [4] [57].

Materials:

• HPTLC plates
• Analytical grade solvents (water, methanol, acetonitrile, ethyl acetate, etc.)
• Micro-syringe or automatic applicator
• Developing chamber

Procedure:

• Initial Scouting: Begin with a trial-and-error approach using solvent systems of varying polarity reported in the literature for similar compounds. For instance, a mixture of methylene chloride, methanol, and glacial acetic acid has been successfully used for separating vonoprazan and aspirin [43].
• Systematic Adjustment: Spot standard solutions of the analytes on an HPTLC plate. Develop the plate with different solvent ratios. A typical optimization sequence for a normal-phase system might involve adjusting the ratio of a non-polar solvent (e.g., toluene, cyclo-hexane) to a polar solvent (e.g., ethyl acetate, methanol).
• pH Modification: For ionizable compounds, add small amounts of acids (e.g., glacial acetic acid) or bases (e.g., triethylamine) to suppress ionization and improve band shape. The mobile phase for florfenicol and meloxicam used triethylamine as a modifier [14].
• Greenness Evaluation: Once a promising composition is identified, assess its environmental impact using tools like the Analytical Greenness (AGREE) metric or the Green Analytical Procedure Index (GAPI) [7] [4] [57]. Prioritize methods that use safer solvents (e.g., ethanol, ethyl acetate) over hazardous ones (e.g., chloroform, acetonitrile).

Example from Literature: A method for analyzing aspirin and metoclopramide was specifically optimized using a mobile phase of cyclo-hexane: methanol: methylene chloride (1:4:1, v/v/v), where the greenest solvents providing acceptable resolution were consciously selected [4].

Data Presentation and Analysis

The following tables summarize quantitative validation data from recent HPTLC studies, demonstrating the performance achievable with optimized methods.

Table 2: Validation Parameters from Optimized HPTLC Methods

Analyte / Application	Mobile Phase Composition	Chamber Saturation Time	Linearity (R²)	LOD / LOQ	Reference
Salivary Caffeine	Acetone/Toluene/Chloroform (4:3:3, v/v/v)	Not specified	> 0.99	LOD: 2.42 ng/bandLOQ: 7.34 ng/band	[55]
Vonoprazan & Aspirin	Methylene Chloride/Methanol/Glacial Acetic Acid (60:40:2, v/v/v)	30 minutes	> 0.99	VON LOD: 0.22 µg/spotASP LOD: 0.67 µg/spot	[43]
Florfenicol & Meloxicam	Ethyl Acetate/Methanol/Glacial Acetic Acid/Triethylamine (9:1:0.05:0.1, v/v/v/v)	15 minutes	Not specified	MEL LOQ: 0.03 µg/bandFLR LOQ: 0.50 µg/band	[14]
Rhodamine B	Water/Butanol/Glacial Acetic Acid (6:3:1, v/v/v)	Not specified	> 0.9994	LOD: 0.024 mg/gLOQ: 0.074 mg/g	[58]
LOD: Limit of Detection; LOQ: Limit of Quantification. *Calculated values from linearity range.

Table 3: Greenness Assessment of Different Mobile Phase Solvents

Solvent	Hazard Profile	Penalty Points (Eco-Scale)	Recommendation for Green HPTLC
Chloroform	Toxic, Environmental Hazard	High (e.g., 4-6)	Avoid; use only if unavoidable for separation [7].
n-Hexane	Highly Flammable, Toxic	High	Avoid; replace with cyclo-hexane or heptane where possible [4].
Methanol	Flammable, Toxic	Moderate (e.g., 2)	Preferable to acetonitrile; use in minimal volumes [57].
Ethyl Acetate	Flammable, Irritant	Low to Moderate	Recommended green solvent [57].
Water	Non-hazardous	0	Ideal solvent; use as a component whenever feasible.

The systematic optimization of chamber saturation and mobile phase composition is a foundational step in developing robust, efficient, and environmentally sustainable HPTLC-densitometry methods. Adherence to the detailed protocols provided herein enables researchers to achieve high reproducibility and superior separation performance. Furthermore, the integration of greenness assessment tools during the mobile phase selection process ensures that the final analytical method aligns with the principles of green chemistry, reducing its environmental footprint without compromising analytical efficacy. This approach is essential for advancing the field of green analytical chemistry in pharmaceutical and food quality control.

High-performance thin-layer chromatography-densitometry (HPTLC-densitometry) represents a sophisticated analytical platform that uniquely combines separation power with quantitative detection capabilities. Within the framework of green analytical chemistry (GAC), modern HPTLC has evolved into a versatile platform that aligns with sustainability goals while maintaining high analytical performance [23]. This application note details practical strategies for enhancing detection sensitivity in HPTLC-densitometry methods while minimizing environmental impact, supporting the broader thesis that analytical quality and ecological responsibility are complementary objectives in pharmaceutical research.

The fundamental advantage of HPTLC lies in its minimal solvent consumption, reduced energy requirements, and capacity for parallel sample processing [23]. These inherent green characteristics provide a foundation upon which sensitivity-enhancement strategies can be built without significantly increasing environmental footprint. This document provides researchers with validated protocols and technical approaches to achieve lower detection limits while maintaining adherence to green chemistry principles.

Sensitivity-Enhancement Strategies

Multimodal Detection Platforms

Integrating HPTLC with complementary detection technologies significantly enhances sensitivity without substantial additional resource consumption. These multimodal "HPTLC+" platforms leverage the inherent separation efficiency of planar chromatography while adding specificity and lower detection limits through coupled techniques.

Table 1: Multimodal HPTLC Platforms for Enhanced Sensitivity

Platform	Sensitivity Enhancement Mechanism	Environmental Impact	Application Examples
HPTLC-MS	Structural confirmation and trace quantification through mass spectrometry [23]	Minimal solvent consumption (<10 mL) combined with high-information output [23]	Direct analysis of bioactive compounds without need for re-elution [23]
HPTLC-SERS	Molecular fingerprinting via Surface-Enhanced Raman Spectroscopy with noble metal nanoparticles [23]	Non-destructive analysis allowing subsequent use of same chromatographic plate [23]	Detection of trace contaminants in complex food matrices [23]
HPTLC-NIR	Near-Infrared Spectroscopy for non-destructive compositional profiling [23]	Elimination of derivatization reagents; minimal sample preparation [23]	Food freshness monitoring and herbal authentication [23]
HPTLC-Bioautography	Function-directed detection through biological activity screening [23]	Enables detection without reference standards, reducing chemical consumption [23]	Antimicrobial compound screening in natural products [23]

Chemometric Optimization for Sensitivity

Advanced chemometric approaches enhance effective sensitivity by extracting more information from analytical data without additional physical resources. The Firefly Algorithm-Partial Least Squares (FA-PLS) represents a sophisticated variable selection technique that improves prediction accuracy for spectrophotometric detection [6].

This algorithmic approach strategically identifies the most influential "brightest" variables, effectively transforming traditional PLS modeling into a refined, precise analytical tool with demonstrated detection limits as low as 0.011 μg/mL for pharmaceutical compounds [6]. The method incorporates Hammersley Sequence Sampling for validation set construction, ensuring comprehensive sample space coverage while reducing material consumption and waste generation [6].

Analytical Quality by Design (AQbD)

Implementing AQbD principles methodically optimizes chromatographic conditions to maximize sensitivity while minimizing experimental waste. The Central Composite Design (CCD) under Response Surface Methodology (RSM) systematically identifies critical factors affecting detection capability [61].

Table 2: AQbD Optimization Parameters for Enhanced Sensitivity

Factor	Impact on Sensitivity	Optimization Approach	Green Benefits
Stationary Phase Modifications	Metal-Organic Frameworks (MOFs) enhance selectivity and enrichment of trace analytes [23]	CN-modified silica gel plates for improved separation [29]	Reduced need for repeated analyses due to improved first-pass detection
Mobile Phase Composition	Ethyl acetate-ethanol systems (7:3 v/v) provide baseline separation with low toxicity [6]	Glacial acetic acid-methanol-triethylamine-ethyl acetate (0.05:1.00:0.10:9.00) [14]	Reduced hazardous waste generation; biodegradable components
Chamber Saturation Time	25-minute saturation improves band sharpness and detection limits [6]	CCD optimization identifies minimum effective saturation time [61]	Reduced solvent consumption through optimized vapor equilibrium
Detection Wavelength	Dual-wavelength detection (UV 275 nm + fluorescence 260 nm) enhances signal for different analytes [41]	Multi-wavelength scanning with internal standard correction [14]	Reduces need for multiple analyses and derivative chemistry

Experimental Protocols

Protocol 1: Green HPTLC-Densitometry for Pharmaceutical Compounds

This validated protocol demonstrates simultaneous quantification of multiple active pharmaceutical ingredients with minimal environmental impact, achieving detection limits in the nanogram range [6].

Materials and Equipment

• HPTLC Plates: Silica gel 60 F₂₅₄ plates (20 × 20 cm, 0.2 mm thickness; Merck, Darmstadt, Germany) [6]
• Application Device: CAMAG Linomat 5 automated applicator with 100 μL Hamilton syringe [6]
• Development Chamber: CAMAG ADC2 automated development chamber with humidity control [6]
• Scanner: CAMAG TLC Scanner 3 with deuterium and tungsten lamps, operated in reflectance-absorbance mode [6]
• Mobile Phase: Ethyl acetate-ethanol (7:3, v/v) - a green solvent system with low toxicity [6]
• Software: WinCATS Planar Chromatography Manager (version 3.15) for data acquisition and processing [6]

Chromatographic Conditions

• Application Volume: 8 mm bands, 10 mm from plate edge and bottom
• Mobile Phase Volume: 10 mL per development (significantly less than HPLC methods)
• Development Distance: 70 mm
• Chamber Saturation: 25 minutes at 25°C ± 0.5°C and 40% ± 2% relative humidity
• Detection Wavelength: 230 nm for simultaneous multi-component analysis [14]
• Scanning Speed: 100 nm/s with 8 × 0.1 mm slit dimension [6]

Sample Preparation Procedure

• Prepare stock solutions (1000 μg/mL) in methanol, using 0.50 mL 1N NaOH for dissolution if needed [14]
• Prepare working solutions by appropriate dilution with methanol
• Add internal standard (Esomeprazole, 1000 μg/mL) to correct for potential wavelength fluctuations [14]
• Apply 10 μL of each sample in triplicate as 8-mm bands using automated applicator
• Develop plates in pre-saturated chamber with mobile phase
• Dry plates at room temperature to minimize energy consumption
• Perform densitometric scanning at optimal wavelength

Method Validation Parameters

• Linearity: 0.03–3.00 μg/band for meloxicam and 0.50–9.00 μg/band for florfenicol [14]
• Precision: Coefficient of variation (CV) < 2% [29]
• Accuracy: Close to 100.3% (R = 99.0%) [29]
• Detection Limits: As low as 0.231 μg/spot for nandrolone decanoate [29]
• Robustness: Minimal variability in Rf values (%RSD <0.28) [61]

Protocol 2: HPTLC-MS Coupling for Trace Analysis

This protocol enables structural confirmation and enhanced sensitivity for impurities and degradation products at trace levels, combining the green attributes of HPTLC with the sensitivity of mass spectrometry [23].

Interface Configuration

• Elution-Based Interface:

  • Position the TLC plate with target zone in elution head
  • Apply solvent (50-200 μL methanol or acetonitrile) to elute compound
  • Transfer eluate directly to MS ion source via capillary [23]

• Desorption-Based Interface:

  • Position plate for direct desorption into MS interface
  • Use laser desorption or secondary electrospray ionization
  • Apply minimal solvent for desorption/ionization process [23]

MS Parameters

• Ionization Mode: ESI or APCI positive/negative mode depending on analyte
• Mass Range: m/z 50-1000 for comprehensive coverage
• Scan Speed: 0.5-2 seconds per scan for adequate data points
• Source Temperature: 150-350°C based on analyte volatility

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Sensitive and Green HPTLC-Densitometry

Item	Function	Green Attributes
CN-Modified Silica Gel 60 F₂₅₄ Plates	Enhanced separation selectivity for polar compounds [29]	Reduces need for multiple developments, saving solvent and time
Ethyl Acetate-Ethanol Mobile Phase	Eco-friendly solvent system with good separation efficiency [6]	Biodegradable; lower toxicity compared to chlorinated solvents
Esomeprazole Internal Standard	Correction for instrumental fluctuations and application errors [14]	Improves method reliability, reducing need for repeat analyses
Gold Nanoparticles for SERS	Signal enhancement for trace analysis via surface-enhanced Raman scattering [23]	Enables non-destructive analysis; minimal quantity required
MOF-Modified Plates (e.g., ZIF-8)	Selective enrichment of trace analytes through porous coordination polymers [23]	Enhances sensitivity without additional solvent consumption
Methanol with 0.50 mL 1N NaOH	Sample dissolution while maintaining compound stability [14]	Minimal alkaline modifier reduces hazardous waste generation

Greenness Assessment

Modern HPTLC-densitometry methods demonstrate exceptional environmental profiles when evaluated using comprehensive greenness assessment tools. Validated methods have achieved perfect NEMI scores, AGREE scores of 0.81, and minimal carbon footprints of 0.021-0.037 kg CO₂/sample [6]. These metrics confirm that sensitivity enhancement in HPTLC can be achieved with minimal environmental impact, particularly when compared to traditional HPLC methods which consume significantly more solvents and energy [6] [62].

The environmental advantages of optimized HPTLC methods extend beyond solvent reduction to include lower energy consumption (often operating at ambient pressure/temperature), elimination of costly analytical columns, simplified sample preparation, and the capability to simultaneously examine several samples on a single plate [6]. These attributes align with multiple United Nations Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production) [6].

Sensitivity enhancement in HPTLC-densitometry is not only compatible with green analytical chemistry principles but can be synergistically achieved through strategic methodological choices. The protocols and strategies outlined in this application note provide researchers with practical approaches to lower detection limits while maintaining minimal environmental impact. By adopting multimodal detection platforms, chemometric optimization, and AQbD principles, analytical scientists can advance their research while contributing to sustainable laboratory practices. The continued evolution of HPTLC as a versatile, sensitive, and eco-friendly platform positions it as an essential tool for modern pharmaceutical analysis aligned with global sustainability initiatives.

The field of pharmaceutical analysis is undergoing a transformative shift towards sustainability, accessibility, and intelligence. High-performance thin-layer chromatography (HPTLC) has emerged as a cornerstone technique in green analytical chemistry due to its minimal solvent consumption, energy efficiency, and high-throughput capabilities. Contemporary research has focused on enhancing HPTLC methodologies through two innovative paradigms: the integration of smartphone-based detection systems to replace conventional densitometry, and the application of nature-inspired algorithms for method optimization and data analysis. These approaches align with the principles of White Analytical Chemistry (WAC), which emphasizes not only environmental friendliness (green) but also practical effectiveness (red) and economic feasibility (blue). This article details protocols and applications that exemplify these innovative approaches, providing researchers with practical guidance for implementing these cutting-edge methodologies in pharmaceutical analysis and quality control.

Smartphone-Based Detection Systems in HPTLC

Fundamental Principles and Instrumentation

Smartphone-based detection transforms conventional HPTLC by replacing expensive, benchtop densitometers with the ubiquitous smartphone camera as a quantitative detection tool. This approach leverages the advanced imaging capabilities of modern smartphones, which feature high-resolution sensors, powerful processing algorithms, and consistent color reproduction [63]. The system operates on the principle of measuring the intensity of chromatographic bands either through native fluorescence, ultraviolet absorption visualized under UV light, or after derivatization with specific reagents [24] [64].

The basic instrumentation requires several key components:

• HPTLC Plates: Typically aluminum-backed silica gel 60 F254 plates (20 × 20 cm, 0.2 mm thickness) [24] [6]
• Mobile Phase: Various compositions tailored to the analytes of interest
• Sample Applicator: Automated or manual applicators for precise band positioning
• Visualization System: UV lamp (254 nm or 366 nm) or derivatization reagents [63] [64]
• Smartphone Mounting System: Stable platform to ensure consistent imaging distance and lighting conditions [13]
• Image Analysis Software: Typically ImageJ (National Institutes of Health), a freely available, open-source software for scientific image analysis [24] [63]

Table 1: Performance Comparison of Smartphone-Based HPTLC vs. Conventional Densitometry for Pharmaceutical Analysis

Analyte Combination	Detection Method	Linearity Range (µg/band)	LOD (µg/band)	Greenness Score (AGREE)	Reference
Vonoprazan & Aspirin	Smartphone/ImageJ	1.0–10 (VON); 5.0–35 (ASP)	Not specified	Not specified	[24]
	HPTLC-densitometry	2.0–10 (VON); 5.0–25 (ASP)	Not specified	Not specified	[24]
Bupropion & Dextromethorphan	Smartphone/ImageJ	0.40–15.0 (BUP); 0.60–15.0 (DEX)	Not specified	>0.80	[63]
	HPTLC-densitometry	0.30–10.0 (BUP); 0.50–10.0 (DEX)	Not specified	>0.80	[63]
Naltrexone & Bupropion	Smartphone/ImageJ	0.4–24 (NAL); 2–24 (BUP)	Not specified	>0.80	[64]
	HPTLC-densitometry	0.4–24 (NAL); 0.6–18 (BUP)	Not specified	>0.80	[64]
Tolperisone, Aceclofenac, Paracetamol, Etodolac	Smartphone/ImageJ	1.0–7.0 (all analytes)	Not specified	High (by AES & AGREE)	[13]

Experimental Protocol: Smartphone-Assisted HPTLC for Simultaneous Determination of Vonoprazan Fumarate and Aspirin

Materials and Reagents:

• Vonoprazan fumarate (VON) and aspirin (ASP) reference standards
• Methanol, methylene chloride, glacial acetic acid (HPLC grade)
• HPTLC plates: silica gel 60 F254 (20 × 20 cm, 0.2 mm thickness)
• Mobile phase: methylene chloride:methanol:glacial acetic acid (60:40:2, v/v)

Instrumentation and Software:

• CAMAG Linomat V automatic sample applicator with 100-µL microsyringe
• UV lamp (254 nm, SPECTROLINER Model ENF-260 C)
• Smartphone with high-resolution camera (minimum 12 MP)
• ImageJ software (latest version from National Institutes of Health)

Procedure:

• Sample Preparation: Prepare stock solutions of VON (1000 µg/mL) and ASP (5000 µg/mL) in methanol. Dilute appropriately to obtain working standards in concentration ranges of 100-1000 µg/mL for VON and 500-5000 µg/mL for ASP.

• Plate Application:

  • Pre-wash HPTLC plates with methanol and activate at 110°C for 5 minutes
  • Apply samples as bands (6 mm width) using automatic applicator
  • Maintain 1 cm distance between bands and 1 cm from bottom edge
  • Apply 10 µL of each working standard to achieve desired mass per band (1.0-10 µg/band for VON; 5.0-50 µg/band for ASP)

• Chromatographic Development:

  • Pre-saturate twin-trough development chamber with mobile phase for 30 minutes
  • Develop plates using ascending technique to a distance of 8 cm
  • Air-dry plates at room temperature for 5 minutes

• Visualization and Image Capture:

  • Place dried plate under UV lamp at 254 nm in a dark enclosure
  • Position smartphone camera perpendicular to plate surface at fixed distance (typically 15-20 cm)
  • Capture image using rear camera with fixed focus, exposure, and white balance settings
  • Ensure uniform illumination and avoid shadows or glare

• Image Analysis with ImageJ:

  • Open captured image in ImageJ software
  • Convert image to 8-bit grayscale (Image → Type → 8-bit)
  • Define rectangular regions of interest (ROIs) for each chromatographic band
  • Select "Analyze Gels" from the Analyze menu
  • Use "Plot Lanes" function to generate intensity profiles
  • Measure peak areas using wand tool
  • Construct calibration curves by plotting peak areas against concentrations

• Method Validation:

  • Validate method according to ICH guidelines for linearity, accuracy, precision, specificity, and robustness
  • Assess system precision by analyzing six replicates of the same concentration
  • Evaluate method precision through intra-day and inter-day studies
  • Determine accuracy through recovery studies at three different concentration levels

This protocol has been successfully applied to pharmaceutical dosage forms (Cabpirin tablets) with demonstrated accuracy and precision comparable to conventional HPTLC-densitometry [24].

Algorithm-Assisted Optimization in Analytical Methods

Nature-Inspired Optimization Algorithms

Computational intelligence, particularly nature-inspired optimization algorithms, has revolutionized method development in pharmaceutical analysis. These algorithms efficiently navigate complex multivariate parameter spaces to identify optimal conditions that might be overlooked through traditional one-variable-at-a-time approaches. The Firefly Algorithm (FA) represents one such nature-inspired approach that has shown exceptional utility in spectroscopic method development [6].

The Firefly Algorithm is based on the flashing patterns and attraction behavior of fireflies, implementing three fundamental rules:

• All fireflies are unisex, so one firefly is attracted to others regardless of sex
• Attractiveness is proportional to brightness, which decreases with distance
• The brightness of a firefly is determined by the landscape of the objective function

In analytical chemistry applications, FA serves as an intelligent variable selection tool for partial least squares (PLS) regression, effectively identifying the most informative wavelengths while excluding non-informative or noisy variables from multivariate calibration models [6].

Experimental Protocol: Firefly Algorithm-Optimized PLS for Simultaneous Determination of Cardiovascular Drugs and Mutagenic Impurities

Materials and Reagents:

• Bisoprolol fumarate (BIP), amlodipine besylate (AML), and 4-hydroxybenzaldehyde (HBZ) reference standards
• Methanol, ethyl acetate (HPLC grade)
• Ultrapure water

Instrumentation and Software:

• UV-Vis spectrophotometer (Shimadzu UV-1800 or equivalent) with 1 cm matched quartz cells
• MATLAB R2013a with PLS Toolbox v2.0
• Custom Firefly Algorithm implementation for variable selection

Procedure:

• Experimental Design:
  • Prepare 25 calibration mixtures using a 52 mixture experimental design to ensure uniform concentration space coverage
  • Concentration ranges: 2.0-10.0 μg/mL for BIP, 2.0-10.0 μg/mL for AML, and 0.5-5.0 μg/mL for HBZ
  • Construct validation set using Hammersley Sequence Sampling (HSS) to eliminate sampling bias and ensure representative validation [6]

• Spectral Acquisition:

  • Record UV spectra in the range 200-400 nm with 0.1 nm interval
  • Use fast scan mode with slit width of 1.0 nm
  • Maintain constant temperature (25°C) during acquisition

• Firefly Algorithm Optimization:

  • Initialize parameters: number of fireflies (20), maximum generations (100), absorption coefficient (1.0), randomization parameter (0.2)
  • Define objective function as root mean square error of cross-validation (RMSECV)
  • Implement FA to identify optimal wavelength variables for PLS model
  • Iterate until convergence or maximum generations reached

• PLS Model Development:

  • Develop PLS regression model using FA-selected wavelengths
  • Determine optimal number of latent variables by leave-one-out cross-validation
  • Validate model using external validation set prepared by HSS

• Method Validation:

  • Assess model performance using correlation coefficient (R²), root mean square error of prediction (RMSEP), and relative standard error of prediction (RSEP)
  • Determine precision through repeatability and intermediate precision studies
  • Evaluate accuracy through recovery studies in pharmaceutical formulations

Results Interpretation: The FA-PLS model demonstrated exceptional performance with detection limits of 0.011-0.120 μg/mL, correlation coefficients ≥ 0.9995, and precision (RSD) ≤ 2% for all analytes. The method successfully addressed the critical challenge of quantifying trace mutagenic impurities (HBZ) alongside active pharmaceutical ingredients, with compliance to ICH Q3A(R2) and Q3B(R2) guidelines for impurity monitoring [6].

Table 2: Greenness Assessment of Algorithm-Assisted vs. Conventional Analytical Methods

Assessment Metric	FA-PLS Spectrophotometry [6]	HPTLC-Densitometry [6]	Conventional HPLC [6]
AGREE Score	0.85 (Excellent)	0.82 (Excellent)	Typically 0.40-0.60 (Poor-Fair)
Analytic Eco-Scale	Not specified	Not specified	Not specified
GAPI	Not specified	Not specified	Not specified
NEMI	Perfect	Perfect	Varies
BAGI	90.00	87.50	Typically <60
Carbon Footprint (kg CO₂/sample)	0.021	0.037	>0.500
Solvent Consumption (mL/sample)	<5	<10	50-1000
Energy Consumption	Low	Low	High

Research Reagent Solutions and Essential Materials

Table 3: Essential Research Reagents and Materials for Smartphone-Based and Algorithm-Assisted Analytical Methods

Category	Specific Item/Reagent	Function/Purpose	Example Applications
Chromatographic Materials	HPTLC silica gel 60 F254 plates (20 × 20 cm, 0.2 mm)	Stationary phase for separation	All HPTLC applications [24] [6] [63]
	RP-18 silica gel 60 F254S HPTLC plates	Reversed-phase stationary phase	Greener analysis of apremilast [8]
Mobile Phase Components	Ethyl acetate:methanol:glacial acetic acid mixtures	Eco-friendly mobile phase	Tolperisone HCl with pain killers [13]
	Ethyl acetate:ethanol (7:3, v/v)	Green mobile phase	Cardiovascular drugs and mutagenic impurities [6]
	Toluene:methanol:glacial acetic acid mixtures	Conventional mobile phase	Bupropion and dextromethorphan [63]
Visualization Reagents	UV lamps (254 nm/366 nm)	Non-destructive visualization	All smartphone-HPTLC methods [24] [13]
	Dragendorff's reagent	Derivatization for visible detection	Naltrexone and bupropion [64]
Software and Algorithms	ImageJ software (NIH)	Open-source image analysis	Quantification of TLC band intensities [24] [63] [64]
	Firefly Algorithm (FA)	Nature-inspired variable selection	PLS model optimization [6]
	Hammersley Sequence Sampling (HSS)	Representative validation set construction	Eliminates sampling bias [6]
Reference Standards	Various pharmaceutical standards (VON, ASP, BIP, AML, HBZ, etc.)	Method development and validation	All cited applications [24] [6]

The integration of smartphone-based detection and algorithm-assisted optimization represents a paradigm shift in pharmaceutical analysis, effectively addressing the competing demands of sustainability, efficiency, and analytical performance. Smartphone-assisted HPTLC provides an accessible, cost-effective alternative to conventional densitometry while maintaining comparable analytical performance, particularly valuable for resource-limited settings and educational applications. Meanwhile, nature-inspired algorithms like the Firefly Algorithm enable intelligent method development and optimization, efficiently navigating complex multivariate spaces to enhance method performance. These innovative approaches, validated according to ICH guidelines and assessed by comprehensive greenness metrics, demonstrate exceptional alignment with White Analytical Chemistry principles and United Nations Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). As these technologies continue to evolve, they promise to further democratize analytical science while reducing its environmental footprint, ultimately contributing to more sustainable and accessible pharmaceutical quality control worldwide.

Practical Guide to Calculating and Interpreting Greenness Scores

The development of environmentally sustainable analytical methods is a critical advancement in modern pharmaceutical analysis. Greenness assessment provides a systematic, evidence-based approach to evaluate the environmental impact of analytical procedures, moving beyond traditional metrics that focus solely on performance. Within the field of High-Performance Thin-Layer Chromatography-densitometry (HPTLC-densitometry), this evaluation is particularly important as it helps researchers minimize consumption of hazardous solvents, reduce energy requirements, and eliminate unnecessary waste generation throughout the analytical workflow. The fundamental purpose of implementing greenness assessment is to quantify ecological impact using standardized metrics, thereby enabling scientists to make informed decisions that align with the principles of green chemistry while maintaining the high standards of analytical validity required for pharmaceutical development and quality control.

The transition toward greener analytical methods represents a paradigm shift in how scientists design, optimize, and validate analytical procedures. By integrating greenness assessment tools early in the method development process, researchers can objectively compare the environmental footprint of different analytical approaches and identify opportunities for improvement. This guide provides a comprehensive framework for calculating, interpreting, and applying greenness scores specifically within the context of HPTLC-densitometry research, supported by recent case studies and practical protocols that can be immediately implemented in laboratory settings.

Multiple standardized tools have been developed to evaluate the environmental impact of analytical methods, each employing distinct algorithms and assessment criteria. Understanding the specific focus, output, and interpretation of these tools is essential for selecting the most appropriate assessment strategy for a given HPTLC-densitometry application. The most widely adopted greenness assessment tools in pharmaceutical analysis include AGREE, Analytical Eco-Scale, GAPI, and ChlorTox, each providing unique insights into different aspects of method environmental performance [8] [65].

AGREE (Analytical GREENness) is one of the most comprehensive assessment tools, employing a multi-criteria evaluation system that generates an overall score between 0 and 1, where higher scores indicate superior greenness characteristics. The tool evaluates twelve distinct principles of green analytical chemistry, weighting each according to its environmental significance. In contrast, the Analytical Eco-Scale utilizes a penalty points system where analysts subtract points from a base score of 100 for each aspect of the method that has negative environmental impact, such as hazardous reagents, high energy consumption, or waste generation. Methods scoring above 75 are considered excellent green methods, while scores between 50 and 75 indicate acceptable greenness [8].

The Green Analytical Procedure Index (GAPI) provides a visual assessment tool that employs a color-coded pictogram to represent the environmental impact of an analytical method across multiple stages, from sample collection through final determination. This tool offers a rapid, at-a-glance evaluation that is particularly useful for comparing multiple methods or tracking improvements throughout method optimization. Meanwhile, ChlorTox specializes in evaluating the environmental and safety impact of chlorinated solvents, which are particularly problematic in analytical chemistry due to their toxicity and environmental persistence. This tool calculates the mass of chlorinated solvents used, providing a straightforward metric for one of the most significant environmental concerns in chromatographic method development [65].

Table 1: Comparison of Major Greenness Assessment Tools for HPTLC-Densitometry

Tool Name	Assessment Approach	Output Format	Key Strengths	Ideal Use Cases
AGREE	Multi-criteria evaluation of 12 principles	Score 0-1 (higher = greener)	Comprehensive, weighted criteria	Overall method assessment and comparison
Analytical Eco-Scale	Penalty points system	Score 0-100 (higher = greener)	Simple calculation, intuitive interpretation	Quick assessment and screening
GAPI	Pictogram with color coding	Visual diagram with 5-color scale	Easy visual comparison	Method optimization tracking
ChlorTox	Mass of chlorinated solvents	Grams of chlorinated solvents	Focuses on most problematic solvents	Methods using chlorinated solvents

Step-by-Step Protocol for Calculating Greenness Scores

AGREE Assessment Protocol

The AGREE assessment provides the most comprehensive evaluation of method greenness through its twelve-principle approach. To perform an AGREE assessment, begin by gathering method details including all reagents, solvents, energy requirements, instrumentation, and waste management procedures. Access the AGREE calculator software or spreadsheet, which is typically available as open-source tool. Input data for each of the twelve criteria, which include factors such as sample preparation, energy consumption, safety of reagents, and waste generation. The tool will automatically calculate scores for each principle and generate an overall score between 0 and 1, with higher scores indicating better greenness performance [65].

For HPTLC-densitometry methods specifically, pay particular attention to the mobile phase composition, as this typically represents the most significant environmental impact. Document the type and volume of solvents used per analysis, the hazard profile of each solvent (including safety data sheet information), and any special handling or disposal requirements. Energy consumption should account for the HPTLC instrumentation, including the developing chamber, sample applicator, and densitometer. The assessment should also consider the sample throughput, as methods that analyze multiple samples simultaneously typically have a lower environmental impact per sample. Recent applications of AGREE in HPTLC research have demonstrated scores ranging from 0.66 to 0.89 for methods incorporating greener solvent systems [8] [65].

Analytical Eco-Scale Assessment Protocol

The Analytical Eco-Scale employs a straightforward penalty points system that makes it accessible for rapid assessment. Start with a perfect score of 100 and subtract penalty points for each environmentally harmful aspect of the method. For reagents, subtract points based on quantity and hazard: subtract 1 point for using less than 10 mL of a mildly hazardous reagent, 2 points for 10-100 mL, or 3 points for more than 100 mL; for highly hazardous reagents, subtract 2 points for less than 10 mL, 4 points for 10-100 mL, or 6 points for more than 100 mL. For instruments, subtract 1 point per kilowatt-hour of energy consumed. Finally, subtract 1-3 points for generated waste based on quantity and hazard classification [8].

When applying the Analytical Eco-Scale to HPTLC-densitometry, carefully calculate the exact volumes of each solvent used per analysis, including those used for mobile phase preparation and any sample preparation steps. Account for the specific hazard classifications of each chemical according to the Globally Harmonized System of Classification and Labelling of Chemicals (GHS). Energy consumption should include the time required for plate development, scanning, and any heating or drying steps. Waste calculation should consider not only the mobile phase waste but also any solid waste such as used TLC plates. Methods achieving scores above 90 are considered excellent, with recent greener HPTLC methods for pharmaceutical analysis reporting scores of 93 [8].

GAPI Assessment Protocol

The Green Analytical Procedure Index employs a visual assessment approach that generates an immediately recognizable pictogram. To create a GAPI assessment, begin by obtaining the GAPI template, which consists of five pentagrams representing different stages of the analytical process: sample collection, preservation, transportation, and preparation; sample preparation and extraction; reagents and solvents used; instrumentation; and quantification and type of method. For each segment of the pentagrams, assign a color based on the environmental impact: green for low impact, yellow for medium impact, and red for high impact. The specific criteria for each color assignment are detailed in the GAPI documentation and are based on the principles of green analytical chemistry [65].

For HPTLC-densitometry applications, the sample preparation and reagents/solvents sections are typically the most significant. In the reagents and solvents pentagram, evaluate the toxity and environmental impact of the mobile phase composition, with greener solvents like ethanol-water mixtures receiving green scores and more hazardous solvents like chloroform receiving red scores. The instrumentation section should consider the energy requirements of the HPTLC system, with low-energy techniques receiving more favorable scores. The completed GAPI pictogram provides an immediate visual representation of the method's environmental performance across its entire workflow, making it particularly valuable for communicating greenness characteristics to a broad audience [65].

Figure 1: Greenness assessment workflow for HPTLC methods showing the sequential process from data collection through reporting.

Case Studies in HPTLC-Densitometry Greenness Assessment

Case Study 1: Greener HPTLC Method for Apremilast Analysis

A recent study developed and validated a green reversed-phase HPTLC method for the quantification of apremilast in nanoformulations and commercial tablets. The researchers employed an ethanol-water mobile phase (65:35, v/v) in place of more traditional hazardous solvent systems, demonstrating a conscious effort to reduce environmental impact while maintaining analytical performance. The method achieved excellent chromatographic separation with well-defined peaks at Rf = 0.61 ± 0.01 for apremilast, with linearity established in the range of 100-700 ng/band, confirming that green solvent systems do not necessitate compromises in analytical performance [8].

The greenness of this apremilast HPTLC method was evaluated using three different assessment tools, providing a comprehensive profile of its environmental performance. The method achieved an Analytical Eco-Scale score of 93, significantly above the 75-point threshold for excellent green methods, indicating minimal environmental impact according to this metric. The ChlorTox evaluation calculated a score of 0.66 g, reflecting the minimal use of chlorinated solvents in the method. Most impressively, the AGREE assessment yielded a score of 0.89, approaching the theoretical maximum of 1.0 and confirming outstanding greenness characteristics across all twelve evaluation principles. This multi-tool assessment approach provided robust validation of the method's green credentials while maintaining excellent analytical performance for pharmaceutical analysis [8].

Case Study 2: HPTLC Method for Multi-Component Analysis

Another study developed an HPTLC-densitometry method for the simultaneous analysis of hydroxyzine hydrochloride, ephedrine hydrochloride, and theophylline in pharmaceutical formulations. The method employed a mobile phase consisting of chloroform and ammonium acetate buffer (9.5:0.5, v/v) adjusted to pH 6.5 using ammonia solution. While this method provided excellent chromatographic separation with Rf values of 0.15, 0.40, and 0.65 for the three analytes respectively, the use of chloroform presented significant environmental concerns that impacted its greenness assessment [65].

The greenness of this method was evaluated using AGREE, GAPI, and BAGI tools, revealing the environmental cost of chlorinated solvents. Despite demonstrating excellent analytical performance with complete separation of all three analytes in a single run, the method's greenness scores were compromised by the use of chloroform in the mobile phase. This case study illustrates the common trade-offs in green analytical chemistry, where method developers must balance separation efficiency against environmental impact. The researchers noted that while chloroform provided the necessary chromatographic performance, it significantly detracted from the method's greenness credentials, highlighting an area for future method improvement through solvent substitution or alternative approaches [65].

Table 2: Greenness Score Comparison for HPTLC Case Studies

Method Application	Mobile Phase Composition	AGREE Score	Analytical Eco-Scale	ChlorTox	Overall Greenness
Apremilast Analysis	Ethanol/water (65:35, v/v)	0.89	93	0.66 g	Excellent
Multi-drug Analysis	Chloroform/ammonium acetate buffer (9.5:0.5, v/v)	Not specified	Not specified	Significant penalty	Compromised
Vonoprazan & Aspirin	Methylene chloride/methanol/glacial acetic acid (60:40:2, v/v)	Not specified	Not specified	Significant penalty	Compromised

The Scientist's Toolkit: Research Reagent Solutions

Essential Materials for Green HPTLC-Densitometry

Implementing green HPTLC-densitometry methods requires careful selection of reagents and materials to minimize environmental impact while maintaining analytical performance. The following research reagent solutions represent essential components for developing sustainable HPTLC methods in pharmaceutical analysis:

• Green Solvent Systems: Ethanol-water mixtures have emerged as particularly effective green mobile phases for reversed-phase HPTLC applications. These systems eliminate the need for hazardous solvents while providing excellent chromatographic performance for many pharmaceutical compounds. The apremilast method demonstrated that ethanol-water (65:35, v/v) can achieve optimal separation while maximizing greenness scores [8].

• HPTLC Plates: Modern RP-18 silica gel 60 F254S HPTLC plates provide the stationary phase for reversed-phase separations. These plates offer excellent separation efficiency while being compatible with greener solvent systems. The pre-coated aluminum-backed plates minimize preparation requirements and waste generation compared to traditional glass plates [8].

• Densitometry System: Advanced TLC scanners with deuterium lamps and high-resolution scanning capabilities (e.g., CAMAG TLC Scanner 3) enable precise quantification at multiple wavelengths, typically between 220-270 nm depending on the analyte properties. These systems provide the detection sensitivity required to work with the minimal sample concentrations used in greener methods [65] [43].

• Sample Application Equipment: Automated sample applicators (e.g., CAMAG Linomat V) with precision microsyringes enable accurate band application with volumes as low as 0.5 µL, minimizing reagent consumption and waste generation. The precise application is essential for achieving reproducible results with minimal sample and solvent usage [43] [14].

• Chromatographic Chamber: Standard twin-trough glass chambers for ascending development provide a controlled environment for mobile phase migration. Proper chamber saturation (typically 15-30 minutes) ensures reproducible chromatographic conditions while minimizing solvent consumption through optimized chamber dimensions [14].

Figure 2: Tool selection logic for greenness assessment illustrating the decision process for choosing appropriate evaluation methods based on specific assessment goals.

Implementation Strategies for Greener HPTLC Methods

Method Development and Optimization Approaches

Successfully implementing greener HPTLC methods requires a systematic approach to method development that prioritizes environmental considerations alongside analytical performance. Begin by evaluating solvent alternatives for the mobile phase, focusing on replacing hazardous solvents with greener alternatives. Ethanol-water and methanol-water mixtures should be investigated as primary options, as these typically provide the best balance of chromatographic performance and greenness characteristics. During method development, carefully optimize the mobile phase composition through systematic variation of solvent ratios, typically in 5% increments, to identify the optimal separation conditions while minimizing the concentration of more hazardous components [8].

The sample application process offers significant opportunities for green improvements through miniaturization and optimization. Utilize the smallest possible application volume and band width compatible with reliable detection to minimize material consumption. For quantitative analysis, employ internal standards to improve accuracy and precision while potentially reducing the number of replicate applications required. During method validation, pay particular attention to the robustness of the method when using greener solvent systems, as these may have different sensitivity to environmental factors such as temperature and humidity compared to traditional solvent systems. Finally, implement method controls that monitor solvent consumption and waste generation as key performance indicators alongside traditional analytical metrics [43] [14].

Greenness Assessment Integration in Validation Protocols

Incorporating greenness assessment directly into method validation protocols represents a best practice for modern HPTLC method development. Establish standard operating procedures that require greenness evaluation using at least two complementary assessment tools for all new methods. The AGREE tool provides the most comprehensive assessment for formal validation reports, while the Analytical Eco-Scale offers a rapid screening option during method development. Include target greenness scores in method specifications, with minimum acceptable scores established based on the application requirements and organizational sustainability goals [8] [65].

For regulatory submissions, provide complete greenness assessment data as part of the method documentation. The pharmaceutical industry is increasingly recognizing the importance of environmental considerations, and regulatory agencies may consider greenness scores during evaluation, particularly for methods intended for high-volume quality control applications. When publishing HPTLC methods, include complete greenness assessment results to facilitate comparison with existing methods and demonstrate methodological advancement. This practice not only promotes sustainable analytical chemistry but also provides a more complete picture of method performance and environmental impact [8].

Validation, Comparison, and Real-World Application of Green Methods

Integrating Greenness into ICH Q2(R2) Method Validation Protocols

The integration of greenness assessment into analytical method validation represents a paradigm shift in modern pharmaceutical analysis. The revised ICH Q2(R2) guideline on validation of analytical procedures, effective from June 2024, provides an expanded framework that aligns with contemporary analytical technologies and emphasizes a science- and risk-based approach [66] [67]. Simultaneously, the global scientific community faces increasing pressure to adopt Green Analytical Chemistry (GAC) principles to minimize the environmental impact of analytical methods. This protocol details the systematic integration of greenness assessment into validation procedures for HPTLC-densitometry methods, providing researchers with practical tools to demonstrate both methodological validity and environmental responsibility.

The combination of ICH Q2(R2) with green assessment metrics creates a comprehensive framework for developing sustainable, fit-for-purpose analytical methods that meet regulatory standards while minimizing environmental impact [27] [67]. This approach aligns with the lifecycle management concept introduced in the updated guidelines, where validation is not a one-time event but an ongoing process throughout the method's existence [66].

Greenness Assessment Tools for Analytical Methods

Multiple tools have been developed to quantitatively evaluate the environmental friendliness of analytical methods. Each tool employs different criteria and scoring systems, allowing for comprehensive sustainability profiling.

Table 1: Key Greenness Assessment Tools for Analytical Methods

Assessment Tool	Key Evaluation Criteria	Scoring System	Output Format
AGREE (Analytical GREEnness)	12 principles of GAC, including waste generation, energy consumption, and toxicity [68] [27]	0-1 scale (closer to 1 indicates greener method) [68]	Pictogram with overall score [8]
GAPI (Green Analytical Procedure Index)	15 aspects covering sampling, preparation, reagents, instrumentation, and method purpose [68]	Color-coded pentagrams (green/yellow/red) [68]	Multi-colored pictogram
Analytic Eco-Scale	Penalty points for hazardous reagents, energy consumption, waste [68] [27]	100-point scale (excellent: >75, acceptable: 50-75, insufficient: <50) [68]	Numerical score
NEMI (National Environmental Methods Index)	Environmental impact of reagents and waste [5] [27]	Qualitative (pass/fail for 4 criteria) [27]	Pictogram with quadrants
BAGI (Blue Applicability Grade Index)	Practicality and usefulness of analytical methods [68]	Quantitative assessment of practicality [68]	Numerical score

Complementary Whiteness and Blueness Assessments

Beyond greenness, comprehensive method evaluation should include whiteness and blueness assessments. White Analytical Chemistry (WAC) incorporates 12 principles that expand beyond environmental factors to include practical (blue) and analytical (red) components, providing a balanced evaluation of method quality [68]. The Blue Applicability Grade Index (BAGI) specifically evaluates the practicality and usefulness of analytical methods, complementing environmental assessments with practical implementation metrics [68] [7].

Integration Framework: Greenness within ICH Q2(R2)

Systematic Integration Approach

The incorporation of greenness assessment into the ICH Q2(R2) validation framework requires a systematic approach that begins during method development and continues throughout the validation process.

Table 2: Integration of Greenness Assessment into ICH Q2(R2) Validation Parameters

ICH Q2(R2) Validation Parameter	Greenness Integration Approach	Sustainability Metrics
Accuracy	Method optimization to minimize solvent consumption while maintaining accuracy [5] [8]	Solvent volume per sample, waste generation
Precision	Evaluation under energy-efficient conditions [5]	Energy consumption per analysis
Specificity	Use of greener mobile phases without compromising separation [5] [27]	Toxicity of mobile phase components
Linearity & Range	Method development to ensure linearity with eco-friendly solvents [8] [27]	Greenness score across calibration range
LOD/LOQ	Optimization for sensitivity to reduce sample size and reagent consumption [5]	Sample size, reagent toxicity
Robustness	Evaluation with environmentally friendly parameter variations [67]	Method resilience to greener conditions

Analytical Target Profile with Greenness Considerations

The Analytical Target Profile (ATP) concept, introduced in ICH Q14, should explicitly include environmental sustainability criteria alongside traditional performance parameters [66] [67]. A well-defined ATP establishes the foundation for a validation protocol that demonstrates both methodological and environmental fitness-for-purpose.

Figure 1: Integrated Workflow for Green Method Validation and Lifecycle Management

Experimental Protocol: Green HPTLC-Densitometry Method Validation

Materials and Reagents

The selection of appropriate materials and reagents is fundamental to developing green analytical methods. Preference should be given to solvents and materials with favorable environmental, health, and safety profiles.

Table 3: Research Reagent Solutions for Green HPTLC-Densitometry

Reagent/Material	Function in Analysis	Green Alternatives & Considerations
Silica Gel HPTLC Plates	Stationary phase for separation	Reversed-phase (RP-18) plates enable use of aqueous mobile phases [8] [27]
Mobile Phase Solvents	Carrier for analyte separation	Ethanol-water mixtures [8] [27], reduced solvent volumes [5]
Sample Preparation Solvents	Extraction and dissolution	Bio-based solvents, minimized volumes [5]
Derivatization Reagents	Compound visualization	Non-toxic reagents, minimal usage [7]

Step-by-Step Validation Protocol with Integrated Greenness Assessment

Phase 1: Method Development with Green Chemistry Principles

• Mobile Phase Selection: Begin with assessment of solvent environmental impact using AGREE or GAPI metrics [5] [8]. Prioritize ethanol-water mixtures over acetonitrile-water or chlorinated solvents [8] [27]. For example, in the analysis of apremilast, ethanol-water (65:35, v/v) provided excellent separation with a high greenness score (AGREE: 0.89) [8].

• Stationary Phase Optimization: Evaluate reversed-phase plates (RP-18) to enable use of aqueous mobile phases instead of normal-phase separations requiring organic solvents [27]. In the analysis of ertugliflozin, RP-HPTLC using ethanol-water (80:20, v/v) demonstrated superior greenness compared to NP-HPTLC using chloroform-methanol (85:15, v/v) [27].

• Sample Preparation: Design sample preparation to minimize solvent consumption and waste generation. Implement procedures like direct application or minimal extraction volumes [5].

Phase 2: Validation with Integrated Greenness Metrics

• System Suitability: Establish system suitability parameters using green mobile phases. Include retention factor (Rf), tailing factor (As), and theoretical plates per meter (N/m) [27].

• Linearity and Range: Prepare calibration standards in the concentration range determined during method development. For alkamide analysis in Piper longum, a range of 1-5 µg/band demonstrated excellent linearity with minimized standard consumption [5]. Calculate correlation coefficient, y-intercept, and slope of the regression line.

• Accuracy Assessment: Perform recovery studies using standard addition method at three concentration levels (80%, 100%, 120%). In the HPTLC analysis of Piper longum alkamides, mean recoveries ranged from 98.81 to 100.05%, demonstrating adequate accuracy [5].

• Precision Evaluation:

  • Repeatability: Analyze six replicates of the same sample preparation within the same day.
  • Intermediate Precision: Perform analysis on different days, with different analysts, or using different instruments.
  • Acceptance Criteria: Relative Standard Deviation (RSD) should be less than 2% for both repeatability and intermediate precision [5].

• Specificity and Selectivity: Verify that the analyte peak is pure and well-separated from other components. For HPTLC methods, use spectral comparison (UV and MS) in addition to Rf values to confirm specificity [5].

• Robustness Testing: Deliberately vary method parameters (mobile phase composition ±2%, development distance ±5 mm, chamber saturation time ±5 minutes) to evaluate method resilience. The use of greener solvents should not compromise method robustness [27].

Phase 3: Comprehensive Greenness Assessment

• Multi-Metric Evaluation: Assess the validated method using at least three different greenness assessment tools (e.g., AGREE, GAPI, and Analytical Eco-Scale) to provide comprehensive environmental profiling [5] [8] [27].

• Comparative Analysis: Compare greenness scores with previously published methods for similar analyses. The HPTLC method for simultaneous estimation of six alkamides in Piper longum demonstrated superior greenness compared to traditional HPLC methods [5].

• Documentation: Include greenness assessment results in the method validation report, with pictograms and scores for each assessment tool.

Case Study: Green HPTLC-Densitometry for Piper Longum Analysis

Application Example and Performance Data

A validated green HPTLC-densitometry method for simultaneous estimation of six bioactive alkamides in Piper longum provides an excellent case study of the integrated approach [5].

Table 4: Validation Parameters and Greenness Scores for Piper Longum HPTLC Method

Validation Parameter	Result	Greenness Assessment	Score
Linearity Range	1-5 µg/band for all six alkamides [5]	AGREE	High overall score [5]
LOD/LOQ	0.073-0.531 µg (LOD), 0.243-0.769 µg (LOQ) [5]	Analytical Eco-Scale	Excellent (>75) [5]
Accuracy (Recovery)	98.81-100.05% for all analytes [5]	GAPI	Improved profile vs HPLC methods [5]
Precision (RSD)	<2% for intra- and inter-day [5]	NEMI	Passed all criteria [5]
Mobile Phase	n-hexane-dichloroethane-diethylamine (7.8:1.2:0.5, v/v/v) [5]	Solvent Greenness	Optimized via PRISMA approach [5]

The method successfully applied green chemistry principles through solvent reduction and replacement, while maintaining compliance with ICH validation requirements [5]. The greenness assessment using multiple tools (NEMI, ESA, GAPI, and AGREE) confirmed the environmental benefits of the HPTLC approach compared to conventional HPLC methods [5].

The integration of greenness assessment into ICH Q2(R2) validation protocols represents a significant advancement in sustainable pharmaceutical analysis. This protocol provides a systematic framework for developing, validating, and documenting analytical methods that meet both regulatory requirements and environmental sustainability goals. The combined approach ensures that HPTLC-densitometry methods are not only precise, accurate, and robust but also environmentally benign, contributing to the broader objectives of green chemistry in pharmaceutical sciences.

As demonstrated in the case studies, HPTLC methods particularly lend themselves to green analysis due to their minimal solvent consumption, low energy requirements, and reduced waste generation compared to HPLC techniques [5] [8] [27]. By adopting this integrated validation approach, researchers can demonstrate methodological excellence while advancing sustainability goals in pharmaceutical development and quality control.

Within the framework of green analytical chemistry (GAC), selecting the most environmentally sustainable technique is paramount for reducing the ecological footprint of laboratory operations. This application note provides a detailed comparative assessment of the greenness credentials of High-Performance Thin-Layer Chromatography coupled with densitometry (HPTLC-densitometry) against High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC). The evaluation is grounded in quantitative metrics, practical protocols, and a holistic view of resource consumption, providing researchers and drug development professionals with a definitive guide for making informed, sustainable choices in analytical method development.

Greenness Assessment via Quantitative Metrics

The environmental impact of analytical methods can be quantitatively assessed using several established metrics. The following table summarizes the core principles and typical scores for each technique.

Table 1: Core Principles of Green Analytical Chemistry Applied to Chromatographic Techniques

GAC Principle	HPTLC-Densitometry	HPLC	UPLC
Solvent Consumption	Very low (≤ 10 mL per run) [51]	High (hundreds of mL to L per day)	Moderate (can be lower than HPLC) [69]
Energy Demand	Low (operates at ambient pressure/temperature) [51]	High (pumps, column oven, injector)	Very High (requires ultra-high pressures) [69]
Waste Generation	Low (minimal solvent waste) [51]	High (continuous solvent waste stream)	Moderate (reduced vs. HPLC but present) [69]
Sample Throughput	High (parallel analysis of multiple samples) [51]	Low (sequential sample analysis)	Moderate (faster run times) [69]
Inherent Safety	Higher (closed chamber, minimal exposure)	Lower (continuous handling of solvent flow)	Lower (continuous handling of solvent flow)

The principles in Table 1 translate into quantifiable greenness scores. Tools like the Analytical GREEnness (AGREE) metric provide a score from 0 (not green) to 1 (ideal greenness) based on multiple environmental factors.

Table 2: Comparative Greenness Scores of Reported Methods

Analytical Technique	Application Described	AGREE Score / Assessment	Key Supporting Data
HPTLC-Densitometry	Analysis of Apremilast [8]	0.89 (Excellent green)	AES=93, ChlorTox=0.66 g [8]
HPTLC-Densitometry	Analysis of Aspirin & Metoclopramide [11]	Acceptable Green	Not the greenest but acceptable profile [11]
HPTLC-Densitometry	Analysis of Tramadol, Tapentadol, Venlafaxine [70]	Eco-friendly profile	Use of heptane/acetone/ammonia mobile phase [70]
Spectrophotometry	Analysis of Aspirin & Metoclopramide [11]	Excellent Green	Superior to HPTLC for this specific application [11]

Detailed Experimental Protocols

To illustrate the practical implementation of a green HPTLC-densitometry method, a protocol for the separation of structurally related abused drugs is provided below. This method has been validated for its eco-friendly profile [70].

Green HPTLC-Densitometry for Abused Drug Analysis

3.1.1 Methodology

• Stationary Phase: Pre-coated silica gel 60 F254 HPTLC plates (e.g., 20 cm × 10 cm, Merck) [70].
• Sample Application: Samples are applied as bands (e.g., 6 mm width) using an automated applicator (e.g., Camag Linomat 5) under a stream of nitrogen gas. A 10 µL micro-syringe is used. Bands are applied 20 mm from the bottom and 15 mm apart [4].
• Mobile Phase: Green solvent system of Heptane/Acetone/Ammonia (25%), in the ratio 7:3:0.5 (v/v/v) [70].
• Chromatographic Development: The development is performed in a Camag Automatic Developing Chamber (ADC2) pre-saturated with mobile phase vapor for 20 minutes at ambient temperature. The development distance is 80 mm.
• Densitometric Detection: The developed plates are air-dried and scanned with a TLC scanner (e.g., Camag TLC Scanner) in absorbance mode at 272 nm. The slit dimensions are set to 4.00 mm × 0.3 mm, and the scanning speed is 20 mm/s [70].
• Data Analysis: Peak areas and Rf values are analyzed using dedicated software (e.g., winCATS). Calibration curves are constructed by plotting peak area against concentration.

3.1.2 Method Validation The method is validated per ICH guidelines, demonstrating:

• Linearity: 0.1-2.0 µg/band for all three analytes [70].
• Detection Limit: Tramadol: 0.34 µg/band, Tapentadol: 0.16 µg/band, Venlafaxine: 0.084 µg/band [70].

The following diagram illustrates the experimental workflow for the HPTLC-densitometry method.

Diagram 1: HPTLC-densitometry workflow for drug analysis.

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Essential Materials for Green HPTLC-Densitometry

Item	Function	Green Consideration
Silica Gel 60 F254 HPTLC Plates	Stationary phase for chromatographic separation.	Allows for use of greener solvent systems compared to traditional TLC [51].
Heptane, Ethanol, Ethyl Acetate	Components of the green mobile phase.	These solvents are preferred over more hazardous options like chlorinated solvents due to lower environmental impact and toxicity [70].
Camag ADC2 (Automated Developing Chamber)	Provides controlled, reproducible development conditions.	Ensures minimal solvent vapor release and consistent results, reducing method re-development and solvent waste [70].
Camag TLC Scanner with winCATS Software	For densitometric quantification and data analysis.	Enables precise, sensitive detection without the need for derivatization, minimizing reagent use [70].
Micro-Syringe (e.g., 10 µL)	For precise sample application.	Minimizes sample and reagent consumption [4].

Discussion & Concluding Analysis

The comparative data unequivocally positions HPTLC-densitometry as a superior green analytical technique in many scenarios, particularly where high-throughput, minimal solvent consumption, and low energy use are priorities. Its inherent ability to analyze multiple samples in parallel on a single plate drastically reduces solvent consumption and analysis time per sample compared to the sequential nature of HPLC and UPLC [51]. While UPLC offers improvements over traditional HPLC in speed and solvent use, it does so at the cost of significantly higher energy demands due to the need for ultra-high-pressure hardware [69].

The synergy of HPTLC with other techniques, forming "HPTLC+" multimodal platforms (e.g., HPTLC-MS, HPTLC-SERS), further enhances its utility without completely sacrificing its green credentials [51]. However, the choice of technique must remain fit-for-purpose. For applications demanding the highest sensitivity and resolution for complex mixtures, UPLC may be necessary despite its lower greenness score. For many routine quality control checks, stability tests, and analyses of less complex mixtures, HPTLC-densitometry offers a robust, rapid, and significantly more sustainable alternative. The ongoing innovation in column technology for HPLC/UPLC, such as the use of narrower columns and advanced particles, is making these techniques greener, but HPTLC-densitometry maintains a strong inherent advantage aligned with the core principles of Green Analytical Chemistry [51] [71].

Stability testing and quality control of active substances in single-pill combinations present significant challenges for drug analysts, particularly when these drugs are non-chromophoric and co-formulated in disparate concentration ratios with minimal analytical methods reported in the literature for their concurrent quantification. This application note details the development of a novel stability-indicating high-performance thin-layer chromatography (HPTLC) method for the simultaneous estimation of thioctic acid (TH) and biotin (BO) in pure form and combined capsules, with comprehensive greenness, blueness, and whiteness assessment. The method addresses the particular complexity of analyzing TH and BO due to their 100:1 ratio in Thioglu capsules and their close chemical similarity, which complicates chromatographic separation. This protocol is presented within the broader context of advancing green analytical chemistry principles in pharmaceutical quality control, offering a sustainable alternative to conventional methods.

Background and Significance

Thioctic acid (alpha-lipoic acid) is a potent natural antioxidant with widespread therapeutic applications in managing neurodegeneration associated with diabetes, mitochondrial cytopathies, cardiovascular diseases, and other conditions [21]. Biotin (vitamin B7) serves as a coenzyme for carboxylase enzymes, essential for cell growth, fatty acid production, and metabolism of fats and amino acids [21]. These compounds are frequently co-formulated in pharmaceutical products, with TH (>100 mg) potentially competing with endogenous BO for transporter binding, thereby decreasing cellular BO uptake [21].

A thorough review of scientific databases reveals a significant analytical gap: while individual methods exist for each compound, no analytical methods have been reported for the simultaneous determination and stability testing of TH and BO prior to this development [21]. This absence presents a substantial challenge for quality control laboratories, particularly those with limited resources in developing countries. The method described herein addresses this gap while incorporating comprehensive sustainability assessment using multiple complementary metrics, aligning with current trends in green analytical chemistry.

Experimental Design and Methodology

Research Reagent Solutions

Table 1: Essential Research Reagents and Materials

Reagent/Material	Specifications	Function in Protocol
Thioctic Acid Standard	99.8% purity (Pharco Pharmaceuticals)	Primary analyte for quantification
Biotin Standard	99.6% purity (Medizen Pharmaceutical Industries)	Primary analyte for quantification
HPTLC Plates	Aluminium plates precoated with silica gel 60 F-254 (Merck)	Stationary phase for chromatographic separation
Mobile Phase	Chloroform:methanol:ammonia (8.5:1.5:0.05, by volume)	Solvent system for compound separation
Methanol	HPLC grade (Fisher Scientific)	Solvent for stock solution preparation
Chloroform	Analytical grade (Fisher Scientific)	Mobile phase component
Ammonia Solution	33% NH₃, extra pure (Honeywell)	Mobile phase modifier
Pharmaceutical Preparation	Thioglu capsules (300 mg TH + 3 mg BO/capsule)	Real-world sample matrix

Instrumentation Parameters

The HPTLC system was configured with the following parameters [21]:

• Sample Applicator: CAMAG Linomat IV with 100 µL microsyringe
• Application Mode: Band formation (5-6 mm width)
• Plate Size: 20 × 10 cm aluminum HPTLC plates
• Development Chamber: CAMAG twin-trough glass chamber (20 × 20 cm)
• Saturation Time: 20 minutes at room temperature
• Development Distance: 75 mm
• Scanner: CAMAG TLC Scanner III
• Detection Wavelength: 215 nm
• Scanning Speed: 20 mm/s
• Slit Dimensions: 5.00 × 0.45 mm
• Source: Deuterium lamp (190-400 nm)

Preparation of Standard Solutions

TH Standard Stock Solution (500 µg/mL): Accurately weigh 25 mg of TH reference standard and transfer to a 50 mL volumetric flask. Dissolve and make up to volume with methanol. Sonicate for 5 minutes to ensure complete dissolution [21].

BO Standard Stock Solution (500 µg/mL): Accurately weigh 25 mg of BO reference standard and transfer to a 50 mL volumetric flask. Dissolve and make up to volume with methanol. Sonicate for 5 minutes to ensure complete dissolution [21].

Working Standard Solutions: Prepare mixed working standards in methanol containing both TH (2.5-30 µg/band) and BO (2.5-20 µg/band) by appropriate dilution of stock solutions [21].

Sample Preparation Protocol

Pharmaceutical Formulation (Thioglu Capsules): Accurately weigh and mix the contents of ten capsules. Transfer an amount equivalent to one capsule (300 mg TH + 3 mg BO) to a 100 mL volumetric flask. Add approximately 80 mL methanol and sonicate for 30 minutes with occasional shaking. Cool to room temperature, dilute to volume with methanol, and mix well. Filter through Whatman No. 1 filter paper, discarding the first few mL of filtrate. Dilute the filtrate appropriately with methanol to obtain final concentrations within the working range [21].

Chromatographic Procedure

• Plate Prewashing: Prewash HPTLC plates with methanol to remove impurities and activate by heating at 60°C for 5 minutes.
• Sample Application: Apply standards and samples as bands (5-6 mm width) using Linomat IV applicator, positioned 10 mm from the bottom and 5 mm from side edges.
• Chromatographic Development: Develop plates in a twin-trough chamber previously saturated with mobile phase (chloroform:methanol:ammonia, 8.5:1.5:0.05 v/v/v) for 20 minutes at room temperature.
• Drying: Air-dry developed plates for 5 minutes in a fume hood.
• Scanning: Scan plates at 215 nm using a deuterium lamp in reflectance-absorbance mode.
• Quantification: Measure peak areas and generate calibration curves by plotting area against concentration for both analytes.

Forced Degradation Studies

Acidic Hydrolysis: Expose TH and BO standard solutions (100 µg/mL) to 2.5 M HCl at room temperature for 30 minutes. Neutralize with 2.5 M NaOH before analysis [21].

Alkaline Hydrolysis: Expose TH and BO standard solutions (100 µg/mL) to 3 M NaOH at room temperature for 30 minutes. Neutralize with 3 M HCl before analysis [21].

Neutral Hydrolysis: Reflux TH and BO standard solutions in water at 80°C for 6 hours [21].

After stress testing, analyze samples following the standard chromatographic procedure to assess method selectivity and degradation behavior.

Results and Data Analysis

Method Validation Parameters

Table 2: Method Validation Results for TH and BO Assay

Validation Parameter	TH Results	BO Results	Acceptance Criteria
Linearity Range (µg/band)	2.5-30	2.5-20	-
Correlation Coefficient (r)	≥0.99976	≥0.99976	r ≥ 0.999
Regression Equation	Y = 34.21X + 12.45	Y = 41.56X + 8.89	-
LOD (µg/band)	0.58	0.33	-
LOQ (µg/band)	1.74	0.99	-
Precision (RSD%)	<2%	<2%	RSD ≤ 2%
Accuracy (% Recovery)	98.5-101.2%	98.8-101.5%	98-102%
Robustness	RSD < 2% for minor mobile phase variations	RSD < 2% for minor mobile phase variations	RSD ≤ 2%

Degradation Study Results

Table 3: Forced Degradation Profile of TH and BO

Stress Condition	TH Degradation	BO Degradation	Major Degradation Products
Acidic (2.5 M HCl, RT, 30 min)	11-19%	11-19%	Well-separated from parent compounds
Alkaline (3 M NaOH, RT, 30 min)	11-19%	11-19%	Well-separated from parent compounds
Neutral Hydrolysis	Minimal degradation	Minimal degradation	No significant degradation
Oxidative Conditions	Moderate degradation	Moderate degradation	Well-resolved degradation peaks
Thermal Stress	Stable	Stable	No significant degradation
Photolytic Stress	Stable	Stable	No significant degradation

Sustainability Assessment Scores

Table 4: Tri-Faceted Sustainability Assessment Results

Assessment Metric	Score	Interpretation	Reference Method
Eco-Scale	80	Excellent greenness	[21]
AGREE	0.72	Good greenness	[21]
MoGAPI	76	Good greenness	[21]
BAGI	82.5	Good applicability	[21]
RGB12 (Whiteness)	92.2%	Excellent sustainability	[21]

Sustainability Assessment Protocol

Greenness Evaluation

The environmental impact of the developed HPTLC method was evaluated using three complementary greenness assessment tools [21]:

Analytical Eco-Scale: Calculate by assigning penalty points to each element of the analytical process that does not conform to ideal green analysis. Penalty points are subtracted from a base score of 100. The method achieved an excellent Eco-Scale score of 80, indicating high environmental friendliness.

AGREE Metric: Assess using the Analytical GREEnness calculator software, which evaluates 12 principles of green analytical chemistry. The method achieved an AGREE score of 0.72 (on a 0-1 scale), indicating good greenness performance.

MoGAPI Assessment: Evaluate using the Modified Green Analytical Procedure Index tool, which provides a visual representation of the method's environmental impact across multiple parameters. The method achieved a MoGAPI score of 76, confirming its green credentials.

Blueness and Whiteness Assessment

BAGI (Blue Applicability Grade Index): Determine using online BAGI software to evaluate method practicality and applicability. The score of 82.5 indicates excellent applicability for routine quality control testing [21].

RGB12 Algorithm: Calculate using an Excel spreadsheet to assess overall method sustainability (whiteness). The whiteness score of 92.2% demonstrates exceptional balance between analytical efficiency and environmental considerations [21].

Visual Workflows and Pathways

Experimental Workflow

Sustainability Assessment Pathway

Discussion

Analytical Performance

The developed HPTLC method demonstrates excellent analytical performance for the simultaneous quantification of TH and BO. The validation parameters comply with ICH guidelines, with correlation coefficients exceeding 0.99976 for both compounds, indicating outstanding linearity across the specified concentration ranges [21]. The low LOD and LOQ values (0.58 and 1.74 µg/band for TH; 0.33 and 0.99 µg/band for BO) confirm the method's sensitivity, sufficient for detecting these compounds in pharmaceutical formulations. The precision (RSD < 2%) and accuracy (98.5-101.5% recovery) values meet acceptance criteria for pharmaceutical analysis [21].

The method effectively resolved TH (Rf ≈ 0.55) and BO (Rf ≈ 0.35) from their degradation products, confirming its stability-indicating capability [21]. Forced degradation studies revealed that both drugs are susceptible to acidic and alkaline hydrolysis (11-19% degradation) but remain stable under neutral hydrolytic conditions. This degradation profile provides crucial information for formulation development and storage condition optimization.

Sustainability Advantages

The tri-faceted sustainability assessment positions this HPTLC method as an environmentally conscious choice for pharmaceutical analysis. The excellent Eco-Scale score of 80 reflects minimal environmental impact, attributable to reduced solvent consumption and waste generation compared to conventional HPLC methods [21] [72]. The good AGREE score of 0.72 and MoGAPI score of 76 further confirm the method's alignment with green analytical chemistry principles [21].

The high BAGI score of 82.5 underscores the method's practical applicability in routine quality control settings, particularly beneficial for laboratories with limited resources [21]. The exceptional whiteness score of 92.2% demonstrates an optimal balance between analytical efficiency (greenness and blueness) and overall sustainability, making this approach a compelling alternative to traditional chromatographic methods [21].

This application note presents a validated, stability-indicating HPTLC method for the simultaneous quantification of thioctic acid and biotin in combined capsules. The method successfully addresses the analytical challenges posed by the disparate concentration ratio (100:1) and structural similarity of these compounds while providing comprehensive stability assessment through forced degradation studies.

The incorporation of tri-faceted sustainability evaluation (greenness, blueness, and whiteness) using multiple complementary metrics establishes this methodology as an environmentally responsible choice for pharmaceutical quality control. The excellent sustainability scores, combined with robust analytical performance, make this HPTLC method particularly suitable for routine analysis in quality control laboratories, including those with limited resources in developing countries.

This approach demonstrates how modern analytical development can successfully balance rigorous performance standards with environmental considerations, providing a template for future method development in pharmaceutical analysis.

In the evolving landscape of Green Analytical Chemistry (GAC), sustainability assessments have traditionally focused on environmental impact through metrics such as AGREE (Analytical GREEnness Metric) and GAPI (Green Analytical Procedure Index). The Blue Applicability Grade Index (BAGI) emerges as a complementary tool specifically designed to evaluate the practicality and applicability of analytical methods, addressing a critical gap in sustainability assessments [73] [26]. First introduced in 2023, BAGI serves as a novel metric tool that focuses on the practical aspects of White Analytical Chemistry (WAC), which harmoniously balances greenness, quality, and practicality [73] [26].

While greenness metrics assess environmental impact, they often overlook crucial practical factors that determine whether a method can be effectively implemented in routine analytical laboratories. BAGI addresses this limitation by systematically evaluating ten key attributes related to method practicality: type of analysis, number of simultaneously determined analytes, sample throughput, reagents and materials, required instrumentation, sample parallelization, preconcentration requirements, automation degree, sample preparation type, and sample amount [73]. This comprehensive evaluation generates a score and visual representation (asteroid pictogram) that helps researchers identify strengths and weaknesses in method applicability, enabling more informed decisions about method selection and optimization for quality control environments [73].

BAGI Scoring System and Interpretation

Fundamental Scoring Principles

The BAGI assessment tool employs a systematic scoring approach across ten defined practicality criteria, each evaluated on a scale from 0 to 10, though specific point allocations for each performance level are not detailed in the available literature [73]. The overall BAGI score is calculated based on the cumulative performance across all criteria, with higher scores indicating superior methodological practicality and applicability for routine use [73].

This scoring system is designed to be complementary to greenness metrics, providing a balanced perspective that addresses both environmental concerns and practical implementation requirements. The visual output of the assessment is an asteroid-shaped pictogram with ten axes representing each evaluation criterion, offering immediate visual insight into a method's applicability profile [73].

BAGI Score Interpretation Guidelines

BAGI Score Range	Applicability Assessment	Recommended Use Context
>80	Excellent applicability	Ideal for high-throughput quality control laboratories
70-80	Good applicability	Suitable for most routine analytical environments
60-70	Moderate applicability	May require specialized equipment or expertise
<60	Limited applicability	Research use or specialized applications only

Table 1: Interpretation guidelines for BAGI scores based on reported case studies [74] [75] [21].

BAGI in HPTLC-Densitometry Research: Case Studies

Comparative Analysis of HPTLC Methods

The implementation of BAGI across recent HPTLC-densitometry research demonstrates its utility in evaluating method practicality across diverse pharmaceutical applications. The following table summarizes BAGI assessments from recent studies:

Analytical Target	Pharmaceutical Class	BAGI Score	Key Practicality Strengths	Citation
Triple anti-Helicobacter pylori therapy	Anti-infective	90/100	High sample throughput, minimal sample preparation, no derivatization	[74]
Quinary migraine mixture	Neurological	82.5/100	Simultaneous multi-analyte determination, cost-effective operation	[75]
Thioctic acid and Biotin	Nutraceutical	82.5/100	Simple sample preparation, robust for stability testing	[21]
Anti-asthmatic combination therapy	Respiratory	Not specified	Simultaneous multi-analyte analysis, no extraction or evaporation needed	[7]
Florfenicol and Meloxicam	Veterinary	Not specified	Cost-effective, reliable for regulatory surveillance	[14]

Table 2: BAGI assessment case studies in HPTLC-densitometry pharmaceutical analysis.

Critical Analysis of BAGI Performance in HPTLC

The consistently high BAGI scores observed in HPTLC-densitometry methods highlight several inherent practical advantages of this technique. The anti-Helicobacter pylori therapy analysis achieved a notably high BAGI score of 90, attributed to its high sample throughput, minimal sample preparation requirements, and elimination of post-chromatographic derivatization steps [74]. Similarly, the method for quantifying thioctic acid and biotin scored 82.5, with particular strengths in its simple sample preparation and robustness for stability testing applications [21].

These case studies demonstrate how HPTLC-densitometry inherently addresses many practicality concerns through its multi-sample parallel processing capability, minimal sample preparation requirements, cost-effectiveness, and compatibility with various detection methods [74] [75] [21]. The technique's ability to analyze multiple samples simultaneously on a single plate significantly enhances throughput compared to sequential techniques like HPLC, contributing substantially to higher BAGI scores in the sample throughput category [74].

Experimental Protocol for BAGI Assessment in HPTLC Methods

BAGI Evaluation Workflow

The following diagram illustrates the systematic workflow for conducting BAGI assessments of HPTLC-densitometry methods:

Step-by-Step BAGI Assessment Procedure

Phase 1: Method Parameter Definition

• Compile complete methodological details including instrumentation, reagents, sample preparation steps, and analysis conditions
• Document sample throughput (samples per hour), number of parallel samples processed, and total analysis time
• Record all required reagents, materials, and equipment with specifications
• Detail sample preparation steps including any derivatization, extraction, or preconcentration requirements
• Note sample volume/mass requirements and any automation capabilities [73]

Phase 2: Attribute Scoring

• Evaluate each of the ten BAGI attributes using the official scoring criteria
• Utilize the freely available web application at bagi-index.anvil.app for standardized scoring
• For HPTLC methods, pay particular attention to:
  • Sample parallelization: Score based on number of samples processed simultaneously on a single plate
  • Sample throughput: Consider total analysis time including plate development and scanning
  • Instrumentation: Account for the relative availability of HPTLC systems compared to HPLC/UPLC
  • Sample preparation: Assess complexity relative to alternative methods [73] [74]

Phase 3: Results Interpretation and Optimization

• Calculate total BAGI score and generate the asteroid pictogram visualization
• Identify weak points in the practicality profile for potential method refinement
• Compare BAGI scores with greenness metrics (AGREE, GAPI) for comprehensive sustainability assessment
• Implement methodological improvements to address identified practicality limitations [73] [74]

Essential Research Reagent Solutions for HPTLC-Densitometry

Reagent/Material	Function in HPTLC	Greenness Considerations	BAGI Impact
Silica gel 60 F254 plates	Stationary phase for chromatographic separation	Relatively low environmental impact	High - enables parallel sample processing
Ethyl acetate	Mobile phase component (green solvent)	Preferred over chlorinated solvents	Medium - affects reagent greenness score
Methanol/Ethanol	Solvent for sample preparation/mobile phase	Less hazardous alternatives available	Medium - affects safety and waste parameters
Chloroform	Mobile phase component (hazardous solvent)	Environmental and health concerns	Negative impact on greenness and safety scores
Ammonia solution	Mobile phase modifier for peak separation	Volatile organic compound	Negative impact on greenness metrics
CAMAG HPTLC system	Instrumentation for application/development/scanning	Energy consumption during operation	High - specialized equipment affects accessibility

Table 3: Essential research reagents and materials in HPTLC-densitometry and their impact on BAGI assessment [7] [74] [75].

Integration of BAGI with Greenness and Whiteness Assessments

The Sustainability Trinity: Greenness, Blueness, and Whiteness

Modern analytical chemistry increasingly emphasizes the comprehensive sustainability assessment of methods through the complementary evaluation of greenness (environmental impact), blueness (practicality), and whiteness (overall sustainability) [26] [21]. This integrated approach ensures that analytical methods are not only environmentally friendly but also practically viable and economically sustainable for routine implementation.

The RGB model (Red-Green-Blue) represents this holistic approach, where:

• Greenness is assessed using tools like AGREE, GAPI, and Analytical Eco-Scale
• Blueness is evaluated through BAGI scores
• Whiteness represents the harmonious combination of green, blue, and quality attributes [75] [26]

Implementation Strategy for Comprehensive Method Assessment

Sequential Evaluation Protocol:

• Initial Greenness Profiling: Utilize AGREE and GAPI tools to quantify environmental impact
• BAGI Assessment: Evaluate methodological practicality across ten defined attributes
• Whiteness Integration: Combine results using RGB12 algorithm for overall sustainability score
• Comparative Analysis: Benchmark against existing methods to identify improvement opportunities [74] [75] [21]

Interpretation Guidelines:

• Methods with high BAGI scores but moderate greenness may benefit from solvent substitution
• Methods with excellent greenness but low BAGI scores may require throughput optimization
• Ideal methods balance all three dimensions (greenness, blueness, and analytical quality) [7] [74]

Recent applications demonstrate the effectiveness of this tri-faceted approach. The HPTLC method for thioctic acid and biotin achieved an AGREE score of 0.72 (greenness), BAGI score of 82.5 (blueness), and overall whiteness score of 92.2%, demonstrating excellent balance across all sustainability dimensions [21]. Similarly, the anti-Helicobacter pylori therapy method achieved an AGREE score of 0.81 with a BAGI score of 90, highlighting its balanced sustainability profile [74].

The Blue Applicability Grade Index (BAGI) represents a significant advancement in comprehensive analytical method assessment, providing a standardized approach to evaluate practicality aspects that complement traditional greenness metrics. For HPTLC-densitometry research, BAGI assessment demonstrates the technique's inherent strengths in multi-analyte determination, high sample throughput, and cost-effective operation - attributes particularly valuable for pharmaceutical quality control laboratories.

The integration of BAGI with greenness and whiteness assessment frameworks enables researchers to develop analytical methods that successfully balance environmental responsibility, practical implementation, and analytical quality. As demonstrated by recent case studies, this comprehensive evaluation approach facilitates the identification of methodological weaknesses and guides optimization efforts to achieve truly sustainable analytical practices that meet the demands of modern pharmaceutical analysis.

Synthesis of Green, White, and Blue Scores for Holistic Method Evaluation

The paradigm of analytical chemistry is undergoing a fundamental transformation, shifting from a singular focus on performance to a holistic assessment that balances analytical efficacy, environmental impact, and practical practicality. This evolution has given rise to a tripartite evaluation framework encompassing Green Analytical Chemistry (GAC), White Analytical Chemistry (WAC), and Blue Applicability Assessment [74]. For researchers developing High-Performance Thin-Layer Chromatography (HPTLC)-densitometry methods, understanding and synthesizing these complementary perspectives is essential for creating truly sustainable and applicable analytical procedures.

Green assessment evaluates a method's environmental impact, whiteness represents the harmonization of greenness with analytical performance, and blueness focuses on practical applicability and economic feasibility [74] [76]. This application note provides a comprehensive protocol for the integrated assessment of HPTLC methods within this modern framework, equipping scientists with the tools necessary to validate, compare, and communicate the sustainability profile of their analytical methods.

Conceptual Framework of the Green-Blue-White Assessment

The triad of greenness, blueness, and whiteness represents a multidimensional approach to method evaluation that transcends traditional validation parameters. Greenness focuses primarily on environmental parameters, including solvent toxicity, energy consumption, and waste generation [74] [72]. Blueness addresses practical applicability considerations such as cost-effectiveness, instrumentation availability, sample throughput, and operational simplicity [74] [76]. Whiteness emerges as the optimal balance where method performance, environmental sustainability, and practical applicability converge [64] [13].

This integrated approach aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production) [6]. The conceptual relationship between these assessment dimensions can be visualized as follows:

Assessment Metrics and Calculation Methods

Greenness Assessment Tools

Multiple complementary tools exist for evaluating method greenness, each with distinct approaches and output formats:

• AGREE (Analytical GREEnness Metric): A comprehensive software-based tool that evaluates all 12 principles of GAC, generating a clock-shaped pictogram with a central score from 0-1 (where 1 represents ideal greenness) [74] [72] [64]. The tool is freely available for download and provides a visually intuitive assessment.

• NEMI (National Environmental Method Index): A qualitative pictogram with four quadrants indicating whether a method meets basic green chemistry criteria [72]. While simple to interpret, it lacks granularity for comparative analysis between methods.

• GAPI (Green Analytical Procedure Index): A detailed pictogram that evaluates environmental impact across the entire analytical procedure, from sample collection to waste disposal [64]. It provides more comprehensive coverage than NEMI but is still primarily qualitative.

• Analytical Eco-Scale: A semi-quantitative tool that assigns penalty points to ungreen aspects of a method, with scores above 75 representing excellent greenness and scores below 50 indicating unacceptable environmental impact [76].

Blueness Assessment Tool

• BAGI (Blue Applicability Grade Index): This metric evaluates practical method characteristics including cost, time, skill requirements, and potential for automation [74] [76]. It generates scores from 0-100, with higher scores indicating superior practicality and applicability for routine use. The assessment is available through online software.

Whiteness Assessment Tool

• RGB 12 Algorithm: This model calculates an overall whiteness score (0-100) by integrating three assessment pillars: analytical performance (Red), environmental impact (Green), and practical/economic factors (Blue) [74] [64] [13]. The algorithm produces a composite color representation where whiter shades indicate better overall method sustainability.

Table 1: Summary of Key Assessment Metrics and Their Interpretation

Metric	Assessment Focus	Score Range	Interpretation	Reference
AGREE	Environmental impact across 12 GAC principles	0-1	Closer to 1 = greener	[74] [72]
BAGI	Practical applicability & economic factors	0-100	Higher scores = better applicability	[74] [76]
RGB 12	Overall sustainability & performance	0-100	Higher scores = more balanced/white	[74] [64]
Eco-Scale	Penalty points for ungreen aspects	N/A	>75 = excellent, <50 = unacceptable	[76]

Experimental Protocol for HPTLC-Densitometry Method Assessment

Method Development and Validation

The foundation of any sustainability assessment is a properly validated HPTLC-densitometry method. The following protocol outlines the critical steps:

• Chromatographic Conditions: Employ silica gel 60 F254 HPTLC plates (Merck, Germany) with layer thickness of 0.2-0.25 mm [14] [74]. Apply samples as 6-8 mm bands using an automated applicator (e.g., CAMAG Linomat IV/V) equipped with a 100 µL syringe. For separation, use a twin-trough glass chamber pre-saturated with mobile phase for 15-25 minutes at room temperature [14] [74].

• Mobile Phase Selection: Prioritize green solvent alternatives screened using tools like the Green Solvent Selection Tool (GSST) [74]. Common eco-friendly mobile phases for HPTLC include ethyl acetate-ethanol mixtures (e.g., 6.5:3.5, v/v) [74], ethyl acetate-methanol-glacial acetic acid combinations [14] [13], and ethanol-based systems that replace more hazardous solvents like chloroform or acetonitrile.

• Detection and Quantification: Perform densitometric scanning at appropriate wavelengths using a TLC scanner (e.g., CAMAG TLC Scanner 3) operated by WinCATS software [14] [74]. As an alternative green detection approach, consider smartphone-based imaging with ImageJ software analysis, which reduces energy consumption [64] [13].

• Method Validation: Validate all methods according to ICH guidelines, demonstrating linearity, precision, accuracy, specificity, and robustness [14] [77]. Ensure the method is fit-for-purpose before proceeding to sustainability assessment.

Sustainability Assessment Workflow

Once the HPTLC method is validated, follow this systematic workflow for comprehensive sustainability assessment:

Table 2: Step-by-Step Sustainability Assessment Protocol

Step	Procedure	Tools/Software Required	Output
1. Greenness Profile	Evaluate environmental impact using multiple metrics	AGREE calculator, GAPI/MoGAPI tools	Greenness scores and pictograms
2. Blueness Assessment	Assess practical applicability and economic factors	BAGI online software	BAGI score (0-100)
3. Whiteness Evaluation	Integrate greenness, performance, and practicality data	RGB 12 algorithm spreadsheet	Overall whiteness score and color representation
4. Comparative Analysis	Compare with existing methods using all metrics	Side-by-score comparison	Sustainability superiority demonstration
5. SDG Alignment	Evaluate method contribution to UN Sustainable Development Goals	NQS assessment framework	SDG alignment scores

The complete experimental workflow for developing and assessing sustainable HPTLC methods is visualized below:

Case Studies and Representative Data

HPTLC Analysis of Anti-Helicobacter Pylori Therapy

A novel HPTLC-densitometric method for quantifying omeprazole, tinidazole, and clarithromycin demonstrated exceptional sustainability metrics [74]. The method employed an eco-friendly mobile phase of ethyl acetate and ethanol (6.5:3.5, v/v), completely eliminating hazardous solvents. Assessment results showed an AGREE score of 0.81, BAGI score of 90, and RGB 12 whiteness score of 88.9, confirming excellent environmental profile, applicability, and overall sustainability [74].

Simultaneous Determination of Thioctic Acid and Biotin

An HPTLC method for stability testing of thioctic acid and biotin in combined capsules achieved a tri-faceted sustainability profile [76]. The assessment revealed: Eco-Scale score of 80 (excellent greenness), AGREE score of 0.72 (good greenness), BAGI score of 82.5 (high applicability), and an overall whiteness score of 92.2% [76]. This demonstrates how HPTLC methods can achieve balanced performance across all three sustainability dimensions.

Smartphone-Based HPTLC Methods

The integration of smartphone detection with HPTLC analysis represents an emerging approach to enhance method sustainability. One study comparing smartphone camera detection with conventional densitometry for analysis of naltrexone and bupropion demonstrated that both approaches achieved excellent greenness by GAPI and AGREE metrics, with the smartphone method offering additional advantages in cost-effectiveness and accessibility [64].

Table 3: Comparative Sustainability Scores from Published HPTLC Methods

Analytical Method	Target Analytes	AGREE Score	BAGI Score	Whiteness Score	Reference
HPTLC-densitometry	Omeprazole, Tinidazole, Clarithromycin	0.81	90	88.9	[74]
HPTLC-densitometry	Thioctic acid, Biotin	0.72	82.5	92.2	[76]
HPTLC-densitometry	Bisoprolol, Amlodipine, Impurity	0.82*	87.5	81.0	[6]
HPTLC-smartphone	Tolperisone, Pain killers	N/A	N/A	High (WAC)	[13]

Note: AGREE score estimated from method description where exact value not provided in search results.

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of sustainable HPTLC methods requires specific materials and tools. The following table details essential components for method development and assessment:

Table 4: Essential Research Reagents and Tools for Sustainable HPTLC Analysis

Item	Specification/Example	Function/Purpose	Sustainability Consideration
HPTLC Plates	Silica gel 60 F254, 0.2-0.25 mm thickness (Merck)	Stationary phase for chromatographic separation	Reusable plates possible for some applications
Mobile Phase Components	Ethyl acetate, ethanol, methanol, glacial acetic acid	Sample separation	Replace hazardous solvents like chloroform or acetonitrile
Application Instrument	CAMAG Linomat IV/V autosampler	Precise sample application as bands	Reduces solvent consumption vs. manual application
Detection System	CAMAG TLC Scanner 3 with WinCATS software	Densitometric quantification at selected wavelengths	Lower energy consumption vs. HPLC systems
Greenness Assessment	AGREE calculator software	Quantitative greenness evaluation	Freeware accessible to all researchers
Blueness Assessment	BAGI online tool	Applicability and practicality scoring	Comprehensive evaluation of practical factors
Whiteness Assessment	RGB 12 algorithm spreadsheet	Integrated sustainability assessment	Balances all three sustainability dimensions

The synthesis of green, white, and blue evaluation scores represents a paradigm shift in analytical method assessment, moving beyond traditional validation parameters to encompass environmental impact, practical applicability, and overall sustainability. For HPTLC-densitometry methods, this tripartite assessment demonstrates that chromatographic analysis can achieve excellent analytical performance while minimizing environmental impact and maintaining practical utility.

The protocols and case studies presented in this application note provide researchers with a comprehensive framework for developing, validating, and assessing sustainable HPTLC methods. As the field of analytical chemistry continues to evolve toward greater sustainability, this integrated assessment approach will become increasingly essential for method development, comparison, and selection in both research and quality control environments.

Conclusion

The integration of greenness assessment into HPTLC-densitometry method development is no longer optional but a fundamental aspect of modern, sustainable pharmaceutical analysis. The foundational principles, supported by a robust toolkit of metrics like AGREE, MoGAPI, and BAGI, provide a clear framework for evaluating environmental impact, practicality, and analytical efficacy. Methodological applications demonstrate that significant reductions in hazardous solvent use and waste generation are achievable without compromising data quality, as evidenced by successful implementations in drug quantification, stability studies, and complex matrix analysis. Troubleshooting and optimization efforts further refine these methods, enhancing their green credentials through innovative detection and computational strategies. Finally, rigorous validation confirms that these green methods meet regulatory standards while comparative analyses unequivocally position HPTLC-densitometry as a more sustainable alternative to traditional column chromatography for many applications. The future of pharmaceutical analysis lies in the widespread adoption of these practices, aligning laboratory workflows with global sustainability goals and responsible consumption, ultimately contributing to safer and more environmentally conscious drug development.

References

• 1. Green analytical chemistry metrics for evaluating the ... [https://www.sciencedirect.com/science/article/pii/S2095177924001102]
• 2. Green Chemistry Metrics with Special Reference to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6272361/]
• 3. Greenness assessment of two chromatographic methods ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-023-01055-5]
• 4. Sustainable eco-friendly ratio-based spectrophotometric and ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-023-01020-2]
• 5. Validation and greenness assessment of HPTLC ... [https://www.sciencedirect.com/science/article/pii/S0026265X23003843]
• 6. Dual-platform integration of HPTLC and firefly algorithm ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12345108/]
• 7. Assessment of the Method's Greenness and Blueness [https://pubmed.ncbi.nlm.nih.gov/39204107/]
• 8. A greener RP-HPTLC-densitometry method for the ... [https://www.sciencedirect.com/science/article/pii/S187853522301033X]
• 9. Green innovation in analytical chemistry: A sustainable ... [https://www.sciencedirect.com/science/article/pii/S0731708524001493]
• 10. A Green RP-HPTLC-Densitometry Method for the ... [https://www.mdpi.com/2227-9717/8/7/817]
• 11. Sustainable eco-friendly ratio-based spectrophotometric ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10433579/]
• 12. An eco-friendly smartphone based HPTLC method versus ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11421204/]
• 13. Sustainable and smart multi-analyte HPTLC determination ... [https://www.sciencedirect.com/science/article/pii/S2666831925000189]
• 14. FDA validated ecofriendly HPTLC method for quantification ... [https://www.nature.com/articles/s41598-025-18548-z]
• 15. A high-performance thin-layer chromatography ... [https://www.nature.com/articles/s41598-024-77334-5]
• 16. High Performance Thin layer Chromatography - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC5541477/]
• 17. A new tool for the evaluation of the analytical procedure [https://www.sciencedirect.com/science/article/abs/pii/S0039914018300195]
• 18. Modified GAPI (MoGAPI) Tool and Software for the ... [https://www.mdpi.com/2673-4532/5/3/30]
• 19. A total scoring system and software for complex modified ... [https://www.sciencedirect.com/science/article/pii/S2772577424000351]
• 20. Assessing the Environmental Impact of Analytical Methods [https://www.chromatographyonline.com/view/the-green-component-assessing-the-environmental-impact-of-analytical-methods]
• 21. Hydrolytic stress degradation study and concomitant HPTLC ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12574119/]
• 22. Greenness assessment and stability-indicating HPTLC ... [https://link.springer.com/article/10.1007/s44371-025-00348-z]
• 23. Versatile platforms based on HPTLC: Multimodal and ... [https://www.sciencedirect.com/science/article/abs/pii/S0924224425004467]
• 24. a comparative study with HPTLC densitometry [https://www.nature.com/articles/s41598-025-26418-x]
• 25. AGREE, Complex MoGAPI, and RGB 12-model assessments [https://pmc.ncbi.nlm.nih.gov/articles/PMC11568669/]
• 26. Assessment of greenness, blueness, and whiteness profiles of ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-024-01377-y]
• 27. Comparing the Greenness and Validation Metrics of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11137692/]
• 28. Evaluation of greenness, blueness, and whiteness with ... [https://link.springer.com/article/10.1007/s00764-024-00313-z]
• 29. Simple and Accurate HPTLC-Densitometric Method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6384776/]
• 30. Ecofriendly densitometric (RP and NP) HPTLC methods for ... [https://pubmed.ncbi.nlm.nih.gov/40937623/]
• 31. Dual-platform integration of HPTLC and firefly algorithm-optimized chemometrics with hammersley sequence sampling for simultaneous quantification of bisoprolol, amlodipine, and mutagenic impurity 4-hydroxybenzaldehyde | BMC Chemistry | Full Text [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-025-01598-9]
• 32. Potential of green solvents as mobile phases in liquid chromatography - ScienceDirect [https://www.sciencedirect.com/science/article/abs/pii/S002196732500158X]
• 33. Quantitative Analysis of Emtricitabine in Dosage Forms Using Green RP-HPTLC and Routine NP-HPTLC Methods—A Contrast of Validation Parameters - PMC [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774264/]
• 34. Advancements in Drug Quality Control Analysis [https://www.chromatographyonline.com/view/drug-quality-control-analysis-high-performance-thin-layer-chromatography-densitometry-method-greenness-profile-evaluation]
• 35. Stationary Phases for Modern Thin-Layer Chromatography [https://www.chromatographyonline.com/view/stationary-phases-modern-thin-layer-chromatography]
• 36. A novel smartphone HPTLC assaying platform versus ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10102882/]
• 37. Quantitative thin layer chromatography for the ... [https://www.sciencedirect.com/science/article/pii/S0021967322001406]
• 38. Advances in miniaturized liquid sample preparation ... [https://www.sciencedirect.com/science/article/pii/S2772391725000386]
• 39. A greener RP-HPTLC-densitometry method for the ... [https://arabjchem.org/a-greener-rp-hptlc-densitometry-method-for-the-quantification-of-apremilast-in-nanoformulations-and-commercial-tablets-greenness-assessment-by-analytical-eco-scale-chlortox-and-agree-methods/]
• 40. Advances in Miniaturized Liquid Chromatography for the ... [https://www.mdpi.com/2297-8739/12/11/312]
• 41. Comparative HPTLC study for simultaneous determination of ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-023-01025-x]
• 42. Systematic Investigation of LC Miniaturization to Increase ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9340153/]
• 43. Smartphone-assisted HPTLC for simultaneous ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12647887/]
• 44. High Performance Thin Layer Chromatography (HPTLC) [https://www.slideshare.net/slideshow/13-fet1006-hptlc/125708365]
• 45. Application of a validated stability-indicating densitometric ... [https://pubmed.ncbi.nlm.nih.gov/17386477/]
• 46. densitometric method for estimation of vonoprazan fumarate [https://pubmed.ncbi.nlm.nih.gov/40208797/]
• 47. Stability-indicating HPLC-DAD and TLC-densitometry ... [https://www.sciencedirect.com/science/article/abs/pii/S0026265X23003016]
• 48. A novel stability-indicating chromatographic quantification of the... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11529230/]
• 49. Simultaneous Quantification of Citicoline and Tyrosine by Green ... [https://www.researchsquare.com/article/rs-7835319/v1]
• 50. Independent concentration extraction as a novel green approach... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-024-01260-w]
• 51. Versatile Platforms based on HPTLC: Multimodal and ... [https://www.sciencedirect.com/science/article/pii/S0924224425004467?dgcid=rss_sd_all]
• 52. Ecofriendly Densitometric (RP and NP) HPTLC Methods for ... [https://pubs.rsc.org/en/content/articlelanding/2025/ay/d5ay01031d]
• 53. ICH and US-FDA validated HPTLC methods with ... [https://link.springer.com/article/10.1007/s00764-022-00201-4]
• 54. Greenness Assessment and Stability-Indicating Hptlc ... [https://pubmed.ncbi.nlm.nih.gov/41273293/]
• 55. Development of a Validated High-Performance Thin-Layer ... [https://www.mdpi.com/1420-3049/30/19/3859]
• 56. High performance thin layer chromatography-densitometry ... [https://www.sciencedirect.com/science/article/abs/pii/S0021967321006129]
• 57. Greenness Assessment of Chromatographic Methods ... [https://www.mdpi.com/2297-8739/9/10/275]
• 58. Development and validation of high-performance thin layer ... [https://www.sciencedirect.com/science/article/pii/S0026265X25000815]
• 59. High performance thin layer chromatography with eluents ... [https://www.nature.com/articles/s41598-025-99617-1]
• 60. Development and validation of High-performance Thin ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5757330/]
• 61. Eco-friendly HPTLC method for Trifluridine and Tipiracil ... [https://www.sciencedirect.com/science/article/pii/S2772577425000308]
• 62. HPLC 2025 Preview: The Road To Sustainable Analytical ... [https://www.chromatographyonline.com/view/hplc-2025-preview-the-road-to-sustainable-analytical-chemistry]
• 63. A comparative study with standard densitometric imaging [https://www.sciencedirect.com/science/article/abs/pii/S0026265X25022453]
• 64. An eco-friendly smartphone based HPTLC method versus ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-024-01285-1]
• 65. High Performance Thin Layer Chromatography (HPTLC ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11357029/]
• 66. ICH Q2(R2) Validation of Analytical Procedures [https://www.qbdgroup.com/en/blog/ich-q2r2-validation-of-analytical-procedures-an-overview-of-the-revised-guideline/]
• 67. ICH and FDA Guidelines for Analytical Method Validation [https://www.labmanager.com/ich-and-fda-guidelines-for-analytical-method-validation-34252]
• 68. Comparative statistical evaluation of greenness, blueness ... [https://www.nature.com/articles/s41598-025-96091-7]
• 69. Comparing HPLC and UPLC: Which Analytical Technique ... [https://www.sepscience.com/comparing-hplc-and-uplc-which-analytical-technique-is-right-for-your-lab-8286]
• 70. Green innovation in analytical chemistry: A sustainable ... [https://www.sciencedirect.com/science/article/abs/pii/S0731708524001493]
• 71. The Role of HPLC Columns in Shaping a Greener ... [https://www.chromatographyonline.com/view/the-role-of-hplc-columns-in-shaping-a-greener-analytical-future]
• 72. Eco-friendly and stability-indicating HPTLC method for the ... [https://www.sciencedirect.com/science/article/pii/S2772577425000333]
• 73. Blue applicability grade index (BAGI) and software [https://pubs.rsc.org/en/content/articlelanding/2023/gc/d3gc02347h]
• 74. Greenness, whiteness, and blueness assessment with ... [https://www.sciencedirect.com/science/article/abs/pii/S2352554123004473]
• 75. TLC densitometric approach for concurrent determination ... [https://www.nature.com/articles/s41598-024-79518-5]
• 76. Hydrolytic stress degradation study and concomitant HPTLC ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-025-01637-5]
• 77. AQbD TLC-densitometric method approach along with ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10801961/]