. Sustainable Chemistry and Pharmacy. 2026;51:102410.
The transition from Green to sustainable chemistry demands a paradigm shift in how analytical methodologies are evaluated, moving beyond isolated performance metrics toward holistic life-cycle assessment. In this study, we introduce a novel Sustainability Assessment Framework (Greenness, Applicability, Sustainability) to rigorously benchmark analytical strategies. As a proof-of-concept, the framework was applied to a complex pharmaceutical challenge: the impurity profiling of paracetamol (PAR) along with three official impurities: para-aminophenol (PAP), para-nitrophenol (PNP), and para-chloroacetanilide (PCA), and two co-formulated drugs ibuprofen (IBU) and chlorzoxazone (CHZ). Two chemometric models, partial least squares (PLS) and artificial neural networks (ANN), were utilized to achieve this purpose. Furthermore, both models were subjected to a variable selection process using genetic algorithm (GA) to identify the most significant wavelengths. The genetic algorithm-optimized neural network (GA-ANN) demonstrated superior predictive accuracy for the six-component mixture. The core innovation of this work lies in the comparative application of the Sustainability Assessment Framework, benchmarking the proposed chemometric method against a reference HPLC method using eight state-of-the-art metrics. Greenness was evaluated via Analytical Eco-Scale, GAPI, and AGREE; Applicability via BAGI, RGB12 algorithm, and EPPI; and Sustainability via the Carbon Footprint, NQS Index, and %Circularity. This study establishes the Framework as a robust prototype for modern quality control, validating chemometrics not merely as an alternative technique, but as a superior sustainable evolution aligned with the principles of the circular economy.