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

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dc.contributor.authorLapcharoensuk, Ravipat
dc.contributor.authorFhaykamta, Chawisa
dc.contributor.authorAnurak, Watcharaporn
dc.contributor.authorChadwut, Wasita
dc.contributor.authorSitorus, Agustami
dc.date.accessioned2026-08-06T10:41:06Z
dc.date.available2026-08-06T10:41:06Z
dc.date.issued2023-03-01
dc.description.abstractThe 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.
dc.identifier.citationFoods, 12(5), 2023
dc.identifier.doi10.3390/foods12050955
dc.identifier.issn23048158
dc.identifier.other2-s2.0-85150179521
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14321
dc.sourceFoods
dc.subjectbok choi
dc.subjectmachine learning
dc.subjectNIR spectroscopy
dc.subjectpesticide
dc.titleNondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach
dc.typeArticle

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