A Sustainable ATR-FTIR Spectroscopy Approach Coupled with AI-Driven Chemometrics for Rapid Quality Assessment of Kanchanar Guggulu Formulations

Technical Summary: A Sustainable ATR-FTIR Spectroscopy Approach Coupled with AI-Driven Chemometrics for Rapid Quality Assessment of Kanchanar Guggulu Formulations

Problem Statement
Kanchanar Guggulu (KG), a classical Ayurvedic polyherbal formulation, is widely utilized for managing glandular and metabolic disorders, including benign prostatic hyperplasia and polycystic ovary syndrome. Despite its therapeutic relevance, ensuring the quality and authenticity of commercially available KG products remains a significant challenge. Variability in raw materials, geographical origins, manufacturing processes, and storage conditions frequently leads to inconsistencies in chemical composition. Conventional quality control methods often rely on macroscopic or organoleptic evaluations, which are subjective and insufficient for detecting subtle compositional differences or adulteration. Furthermore, traditional analytical techniques (e.g., chromatography) often involve hazardous reagents, complex sample preparation, and high energy consumption, conflicting with the principles of Green Analytical Chemistry (GAC). There is a critical need for rapid, non-destructive, and environmentally sustainable analytical strategies to standardize these complex herbal matrices.

Methodology
The study developed an analytical strategy integrating Attenuated Total Reflection–Fourier Transform Infrared (ATR-FTIR) spectroscopy with AI-driven chemometric analysis.

• Sample Collection: Thirteen samples were analyzed, comprising twelve commercial KG products sourced from various Indian retailers and one authenticated in-house reference sample prepared according to classical Ayurvedic guidelines.
• Reference Preparation: The in-house reference was meticulously prepared using authenticated botanical ingredients (including Bauhinia variegata, Commiphora mukul, and others) following pharmacopeial standards. Physicochemical parameters (e.g., ash content, extractive values, friability, disintegration time) were validated to confirm its suitability as a standard.
• Spectroscopic Analysis: Powdered samples were analyzed directly without solvent extraction. Spectra were recorded in the 4000–400 cm⁻¹ range using a Jasco 4700 ATR spectrometer with 16 scans at 4 cm⁻¹ resolution.
• Chemometric Analysis: Principal Component Analysis (PCA), an unsupervised machine learning technique, was applied to the spectral data to reduce dimensionality and identify clustering patterns. This allowed for the differentiation of samples based on their chemical fingerprints.
• Sustainability Assessment: The environmental and practical viability of the method was evaluated using four specific metrics: Complex Green Analytical Procedure Index (Complex GAPI), Analytical GREEnness (AGREE), Blue Applicability Grade Index (BAGI), and the RGB12 model (White Analytical Chemistry framework).

Key Contributions and Results

• Spectral Fingerprinting: ATR-FTIR successfully captured distinct chemical fingerprints for KG formulations. Characteristic absorption bands were identified, including O–H stretching (3200–3600 cm⁻¹), C–H stretching (2800–3000 cm⁻¹), C=O stretching (~1700 cm⁻¹), and C–O stretching (1000–1300 cm⁻¹), reflecting the presence of phenolics, terpenoids, and glycosides.
• Differentiation via PCA: The PCA model effectively differentiated the commercial samples from the authenticated reference. The first two principal components accounted for approximately 40% of the total variance. Most commercial samples clustered closely with the reference, indicating chemical similarity. However, specific samples (identified as 1, 13, and 14 in the study) appeared as outliers, suggesting compositional deviations potentially due to adulteration or processing variations.
• Sustainability Metrics: The proposed method demonstrated high sustainability scores across all assessment tools:
  • Greenness (AGREE/Complex GAPI): The method required minimal reagents, generated negligible waste, avoided toxic solvents, and had low energy requirements.
  • Blueness (BAGI): The approach scored highly on practical applicability, offering rapid analysis, ease of automation, and high sample throughput.
  • Whiteness (RGB12): The holistic evaluation confirmed a balanced performance in environmental impact, analytical reliability, and operational efficiency.

Significance and Claims
The authors claim that the integration of ATR-FTIR spectroscopy with chemometric analysis provides a robust, rapid, and cost-effective alternative to conventional chromatographic methods for the quality control of Ayurvedic formulations. The study positions this approach as a "data-driven analytical strategy" that enhances the scientific validation of Traditional, Complementary, and Integrative Medicine (TCIM).

The paper emphasizes that this method is not only effective for detecting compositional variations and ensuring product authenticity but also aligns with modern principles of sustainable chemistry. By eliminating hazardous solvents and reducing sample preparation time, the workflow offers an environmentally responsible solution for routine quality assessment. The authors conclude that such AI-assisted, spectroscopic frameworks are essential for advancing the standardization and global acceptance of traditional herbal medicines, providing a reliable tool for distinguishing authentic formulations from substandard or adulterated commercial products.