Non‐destructive detection and recognition of pesticide residue levels on cauliflowers using visible/near‐infrared spectroscopy combined with chemometrics

Abstract

AbstractIn this study, two prediction models were developed using visible/near‐infrared (Vis/NIR) spectroscopy combined with partial least squares discriminant analysis (PLS‐DA) and least squares support vector machine (LS‐SVM) for the detection of pesticide residues of avermectin, dichlorvos, and chlorothalonil at different concentration levels on the surface of cauliflowers. Five samples of each of the three different types of pesticide were prepared at different concentrations and sprayed in groups on the surface of the corresponding cauliflower samples. Utilizing the spectral data collected in the Vis/NIR as input values, comparison and analysis of preprocessed spectral data, and regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used in turn to downscale the data to select the main feature wavelengths, and PLS‐DA and LS‐SVM models were built for comparison. The results showed that the RC‐LS‐SVM was the best discriminant model for detecting avermectin residues concentration on the surface of cauliflowers, with a prediction set discriminant accuracy of 98.33%. For detecting different concentrations of dichlorvos, the SPA‐LS‐SVM had the best predictive accuracy of 95%. The accuracy of the model based on CARS‐PLS‐DA to identify chlorothalonil at different concentration levels on cauliflower surfaces reached 93.33%. The results demonstrated that the Vis/NIR spectroscopy combined with chemometrics could quickly and effectively identify pesticide residues on cauliflower surfaces, affording a certain reference for the rapid recognition of different pesticide residue concentrations on cauliflower surfaces.Practical ApplicationVis/NIR spectroscopy can detect the concentration levels of pesticide residues on the surface of cauliflowers and help food regulators quickly and non‐destructively detect traces of pesticides in food, providing a guarantee for food safety. The technique also provides a basis for determining pesticide residue concentrations on the surface of other vegetables.