Given the global growth in the production and consumption of generic drugs and the increasing risk of counterfeit or substandard pharmaceutical products, the development of novel, rapid, and non-destructive quality control methods has become critically important. In this study, hyperspectral imaging (HSI) in the visible to near-infrared range (Vis-NIR, 400–950 nm), combined with chemometric/machine learning techniques, was employed to assess the active pharmaceutical ingredient (API) content of metformin in powder-based samples. Samples were classified into three dosage groups: standard dose (SD), low non-standard dose (LD), and high non-standard dose (HD). Hyperspectral imaging data were processed using spectral averaging, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and machine learning algorithms including artificial neural network (ANN) and support vector machines (SVM). Results demonstrated that chemometric models, particularly ANN and PLS-DA, could effectively differentiate between the three sample groups with high accuracy. The combination of Vis-NIR HSI and statistical modelling proved to be a powerful tool for detecting metformin dosage levels and distinguishing standard from non-standard pharmaceutical compositions.
Hatefi, F. , Bolhasani, Z. and Parastar, H. (2025). Quality Control of Powdered Metformin Using Hyperspectral Imaging and Machine Learning Models. Applied Chemistry Today, 20(76), 97-116. doi: 10.22075/chem.2025.37869.2370
MLA
Hatefi, F. , , Bolhasani, Z. , and Parastar, H. . "Quality Control of Powdered Metformin Using Hyperspectral Imaging and Machine Learning Models", Applied Chemistry Today, 20, 76, 2025, 97-116. doi: 10.22075/chem.2025.37869.2370
HARVARD
Hatefi, F., Bolhasani, Z., Parastar, H. (2025). 'Quality Control of Powdered Metformin Using Hyperspectral Imaging and Machine Learning Models', Applied Chemistry Today, 20(76), pp. 97-116. doi: 10.22075/chem.2025.37869.2370
CHICAGO
F. Hatefi , Z. Bolhasani and H. Parastar, "Quality Control of Powdered Metformin Using Hyperspectral Imaging and Machine Learning Models," Applied Chemistry Today, 20 76 (2025): 97-116, doi: 10.22075/chem.2025.37869.2370
VANCOUVER
Hatefi, F., Bolhasani, Z., Parastar, H. Quality Control of Powdered Metformin Using Hyperspectral Imaging and Machine Learning Models. Applied Chemistry Today, 2025; 20(76): 97-116. doi: 10.22075/chem.2025.37869.2370
)