Integrating artificial intelligence with spectrophotometry: A novel python framework for automated green assessment...

Integrating artificial intelligence (AI) into green analytical chemistry offers new opportunities for rapid, objective, and reproducible sustainability assessment. In this work, we developed an AI-assisted, Python-based platform for automated, multi-metric evaluation of analytical methods. The developed platform integrates the Green Analytical Procedure Index (GAPI), Analytical GREEnness metric (AGREE), White Analytical Chemistry (WAC), Red analytical performance index (RAPI), and solvent sustainability assessment within a unified computational framework, enabling automated scoring, visualization, and comparative sustainability profiling. The platform was applied to eco-friendly spectrophotometric methods for the simultaneous determination of metoprolol (MET) and hydrochlorothiazide (HYD) in binary mixtures and pharmaceutical dosage forms. Four signal-processing techniques, namely ratio difference (RD), first ratio derivative (RD¹), mean centering (MC), and Fourier self-deconvolution (FSD), were successfully employed to resolve the severe spectral overlap without prior separation. The proposed methods exhibited linear responses over concentration ranges of 5-50 μg/mL for MET and 5-30 μg/mL for HYD, while FSD demonstrated the highest sensitivity, with detection limits of 0.756 and 0.707 μg/mL for MET and HYD, respectively. The methods showed satisfactory accuracy, precision, and reliability in accordance with international validation requirements. The AI-assisted sustainability assessment demonstrated favorable green, white, and solvent sustainability profiles, with minimal solvent consumption and avoidance of hazardous reagents. Importantly, the developed Python code is provided as Supplementary Material, allowing transparent and reproducible implementation and facilitating adaptation to other analytical methodologies. Overall, this work combines automated intelligence-driven sustainability assessment with advanced spectrophotometric analysis, providing a practical and transferable framework for sustainable pharmaceutical analytical chemistry.