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http://www.alice.cnptia.embrapa.br/alice/handle/doc/1177450
Title: | Monitoring pesticides with portable NIR spectroscopy in different intact fruits. |
Authors: | FERREIRA, I. J. S.![]() ![]() COSTA, D. dos S. ![]() ![]() ROLIM, L. A. ![]() ![]() FREITAS, S. T. de ![]() ![]() SOUZA, N. A. C. de ![]() ![]() TERUEL, B. ![]() ![]() |
Affiliation: | IARA JEANICE SOUZA FERREIRA, STATE UNIVERSITY OF CAMPINAS; DANIEL DOS SANTOS COSTA, FEDERAL UNIVERSITY OF VALE DO SÃO FRANCISCO; LARISSA ARAÚJO ROLIM, FEDERAL UNIVERSITY OF VALE DO SÃO FRANCISCO; SERGIO TONETTO DE FREITAS, CPATSA; NATHÁLIA ANDREZZA CARVALHO DE SOUZA, FEDERAL UNIVERSITY OF VALE DO SÃO FRANCISCO; BARBARA TERUEL, STATE UNIVERSITY OF CAMPINAS. |
Date Issued: | 2025 |
Citation: | Journal of Food Composition and Analysis, 108024, July, 2025. |
Description: | This study aimed to investigate the use of portable NIR spectroscopy with data mining techniques for pesticide quantification in cherry tomatoes and strawberries. For each product, reflectance spectra of 240 samples, composed of three fruits and treated with different concentrations of azoxystrobin, chlorothalonil, chlorpyrifos, difenoconazole, lambda-cyhalothrin, or tetraconazole, were obtained in the wavelength range of 900 - 1700 nm, using the DLP NIRscan and FieldSpec 3 spectrometers. Reference analyses were performed using liquid chromatography. Mathematical pre-processing techniques as well as variable selection were applied to the spectral data. The regression models were developed using Partial Least Squares Regression (PLSR), Orthogonal Projection for Latent Structures (OPLS), Random Forest (RF) and Support Vector Machine (SVM) techniques. The OPLS models with selection of RFE or SFM variables were able to quantify pesticides with R2p from 0.80 to 0.96, RMSEP from 0.01 to 0.03, RPDP from 2.24 to 4.76, and R2p from 0.73 to 0.80, RMSEP from 0.06 to 0.12, RPDP from 1.93 to 2.27 in samples of cherry tomatoes and strawberries, respectively. These results show that portable NIR spectroscopy, combined with data mining techniques, holds promise for monitoring pesticide residues in cherry tomatoes and strawberries. |
Thesagro: | Tomate Resíduo Quimico Pesticida Morango |
NAL Thesaurus: | Chemical residues Near infrared radiation Tomatoes Cherry tomatoes Strawberries |
Keywords: | NIR portátil Tomate cereja Aprendizado de Máquina Monitoramento de pesticidas |
DOI: | https://doi.org/10.1016/j.jfca.2025.108024 |
Notes: | On-line. |
Type of Material: | Artigo de periódico |
Access: | openAccess |
Appears in Collections: | Artigo em periódico indexado (CPATSA)![]() ![]() |
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File | Description | Size | Format | |
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Monitoring-pesticides-with-portable-NIR-spectroscopy-in-different-intact-fruits.pdf | 921.16 kB | Adobe PDF | ![]() View/Open |