A Sustainable Green Analytical Framework: Combining UV-Vis, IR, and Fluorescence Spectroscopy with Chemometric and Machine-Learning Models for the Accurate Quantification of Metformin and Sitagliptin

DOI

https://www.doi.org/10.59256/ijrtmr.20260605011

Published In

Volume 6, Issue 5 (2026)

Published Online

September-October 2026

Pages

87-97

Article Type

Review Article

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Pharmaceutical laboratories are under growing pressure to trade solvent-heavy assay methods for greener alternatives that still meet strict quality benchmarks, and this has fed a broader move toward Green Analytical Chemistry (GAC). This review pulls together and critically evaluates published work on low-solvent, sustainable ways of assaying the antidiabetic fixed-dose combination metformin and sidesplitting covering UV-Vis, FT-IR, and molecular fluorescence spectroscopy, used both on their own and coupled with chemometric or machine learning (ML) multivariate calibration. Using a structured search across PubMed/MEDLINE, Scopus, Web of Science, ScienceDirect, and Google Scholar (2010–2026, weighted toward 2020–2026) with clearly defined inclusion and exclusion criteria, 36 primary and methodological sources were identified and compared, extending the ground covered by earlier, narrower surveys of this drug pair. Spectra collected from commercial tablets can be interpreted through Principal Component Regression (PCR), Partial Least Squares (PLS) regression, and, increasingly, artificial neural network (ANN) or support vector machine (SVM) models to disentangle overlapping signals and reduce excipient interference. Provided such methods are validated against ICH Q2(R2) and built within the ICH Q14 Analytical Quality by Design (AQbD) framework, they can match reversed-phase HPLC for linearity, precision, and accuracy while scoring noticeably better on recognized greenness metrics (AGREE, GAPI, ComplexGAPI, Eco-Scale, GEMAM). This review weighs the strengths and shortcomings of each spectroscopic approach, proposes as its central contribution a consolidated, data-fused, AQbD-compliant, green-verified framework, and considers its fit for routine batch release, stability testing, and Process Analytical Technology (PAT), including the newer wave of AI-enabled digital spectroscopy.