Abstract:
The presence of pesticide residues in food crops poses serious health concerns, necessitating precise, rapid and accessible
detection techniques. This study investigates the use of Raman spectroscopy combined with advanced data analysis techniques to
detect and quantify Mancozeb residues in collard greens. The primary objective was to evaluate the viability of this approach
for accurate pesticide residue monitoring in leafy vegetables. Raman spectral data were collected and preprocessed using a
standard normalization technique to reduce spectral noise and enhance signal quality. Dimensionality reduction was achieved
through a statistical method that extracted key spectral features and successfully differentiated control from treated samples,
explaining a combined variance of 86% across the first two principal components. Graphical score plots revealed clear clustering
patterns across various residue concentrations, ranging from 0.01 to 0.5 parts per million, with samples categorized according to
regulatory residue limits. To further assess predictive capability, several machine learning models were developed for classification
and quantification, including deep learning–based and ensemble-based approaches. Among these, the support vector model
achieved the highest classification precision of 95% and demonstrated strong calibration and prediction accuracy. A convolutional
neural network achieved 99% training accuracy and 98% testing accuracy, effectively recognizing complex spectral patterns.
Statistical validation using analysis of variance confirmed that the observed model differences were significant, supporting the
robustness of the selected algorithms. The proposed method accurately quantified Mancozeb residues within the tested range
and demonstrated high sensitivity even at low concentration levels. This study highlights the potential of Raman spectroscopy,
integrated with computational modelling, as a non-destructive, fast and cost-effective tool for pesticide residue detection in food
safety applications.
