Machine Learning–Assisted Raman and Ultraviolet–Visible Spectroscopic Analysis of Mung Plants Exposed to Zinc Oxide Nanoparticles

ABSTRACT This study examines the effects of varying concentrations of zinc oxide nanoparticles (ZnO NPs) on the biochemical profile of mung plants, utilizing Raman and ultraviolet–visible (UV–Vis) spectroscopy combined with machine learning algorithms for data analysis. Mung plants, developed in the lab under optimized growth conditions, were exposed to different ZnO NPs concentrations (0.2, 0.4, 0.6, 0.8, 1.0, 1.2, and 1.4 mM, particle size < 30 nm). UV–Vis measurements show that concentrations of photosynthetic pigments declined with higher ZnO NPs treatment, implying a decrease in photosynthetic efficiency due to oxidative stress. The analysis of acquired Raman spectra shows disruptions in key biochemicals, including carotenoids, pectin, lignin, cellulose, protein, and aliphatics on exposure to nanoparticles, indicating effects on the metabolic processes of plants. To assess these changes, various machine learning algorithms were employed, including unsupervised methods ( k ‐means clustering, density‐based spatial clustering of applications with noise, agglomerative clustering, and principal component analysis) and supervised methods (support vector machine, random forest, k ‐nearest neighbor, decision tree, logistic regression, gradient boosting, and linear discriminant analysis). The support vector machine and random forest models achieved the highest classification accuracy, precision, recall, and f1 scores, effectively differentiating between NPs‐induced biochemical changes. Additionally, unsupervised algorithms revealed distinct clustering patterns, aiding in the identification of NPs treatment effects on plants. These findings demonstrate the potential of integrating confocal micro‐Raman and UV–Vis spectroscopy with machine learning as a rapid, early, nondestructive, and robust tool for providing valuable insights for sustainable agricultural practices.