2025/07/24 by Aishwary Awasthi, Aradhana Tripathi, Chhavi Baran +2
paper · doi:10.1002/jrs.70023
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 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.