Non-destructive detection and recognition of pesticide residue levels on cauliflowers using visible/near-infrared spectroscopy combined with chemometrics.
cauliflowers
chemometrics
concentration
pesticide residue
visible/near-infrared spectroscopy
Journal
Journal of food science
ISSN: 1750-3841
Titre abrégé: J Food Sci
Pays: United States
ID NLM: 0014052
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
revised:
20
06
2023
received:
18
04
2023
accepted:
14
07
2023
medline:
23
10
2023
pubmed:
17
8
2023
entrez:
17
8
2023
Statut:
ppublish
Résumé
In 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 APPLICATION: Vis/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.
Identifiants
pubmed: 37589297
doi: 10.1111/1750-3841.16728
doi:
Substances chimiques
Pesticide Residues
0
tetrachloroisophthalonitrile
J718M71A7A
avermectin
73989-17-0
Dichlorvos
7U370BPS14
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4327-4342Subventions
Organisme : National Natural Science Foundation of China
ID : 31801632
Informations de copyright
© 2023 Institute of Food Technologists.
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