Nondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach

Abstract

The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908–1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.

Systematic approaches to machine learning models for predicting pesticide toxicity

“…These algorithms include the sufficient component artificial neural networks (PC- ), partial least-squares discriminant analysis (PLS-DA), and support vector machines (SVM). A total of 120 bok choi samples were obtained from two small conservatories that developed cultivation of internal individuals [ 46 ]. Sixty samples were allocated to each of the two treatment groups: pesticide and non-pesticide groups.…”

“…In the analysis of an unknown dataset, 40 samples were collected, so after the sample achieved 100 % success, F1-victory. The researchers found that the proposed portable NIR spectrometer, combined with ML algorithms (PLS-DA, SVM, and PC-ANN), could detect chlorpyrifos [ 46 ].…”

“…These algorithms include Partial Least-Squares Discrimination Analysis (PLS-DA), Support Vector Machines (SVM), Artificial Neural Networks ( ), and Principal Component Artificial Neural Networks (PC- ). 120 bok choi samples were collected from two small greenhouses where they were grown in isolation [ 46 ]. There were 60 samples in each of the pesticide and non-pesticide treatment groups.…”

“…A satisfactory F1-score (100%) was obtained after testing the model on an unknown dataset consisting of 40 samples. Researchers have found that chlorpyrifos residues on bok choi could be detected using the suggested portable NIR spectrometer in conjunction with ML algorithms (PLS-DA, SVM, and PC-ANN) [ 46 ].…”

“…These methods can analyze a wide range of data, such as chemical properties and molecular structures, to identify the patterns and relationships between pesticides and their toxicity levels. Using ML, researchers can create predictive models to accurate pesticide toxicity and identify high-risk chemicals [ [45] , [46] , [47] , [48] ]. In this review, we investigated the ML approach for predicting pesticide toxicity.…”