Differentiation of Lupinus and Mimosa by Machine Learning Employing Spectroscopic Data: A Comparative Study

This study investigated the complex chemical composition of species from the genera Lupinus and Mimosa (Fabaceae) from southern Brazil using ultraviolet-visible (UV-vis) and Fourier transform infrared (FTIR) spectroscopy, in conjunction with machine learning techniques. The results revealed that, despite similarities in the presence of secondary metabolites such as flavonoids and alkaloids, the genera can be accurately distinguished. To overcome the high dimensionality and collinearity of spectral data, linear discriminant analysis (LDA) was coupled with variable selection by successive projection algorithm (SPA) and genetic algorithm (GA). These models successfully isolated specific spectral markers, achieving 100% sensitivity, specificity, and accuracy in sample classification. This study highlights the effectiveness of combining accessible spectroscopic techniques with machine learning in plant taxonomy, offering a robust preliminary alternative for species differentiation and enhancing the understanding of chemical diversity within the Fabaceae family.