{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:16:27Z","timestamp":1770891387276,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis\/NIRS and prediction models such as PLSR and ANN. First, Vis\/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350\u20131100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.<\/jats:p>","DOI":"10.3390\/s21093032","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T06:19:11Z","timestamp":1619504351000},"page":"3032","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Evaluation of Different Models for Non-Destructive Detection of Tomato Pesticide Residues Based on Near-Infrared Spectroscopy"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7479-3291","authenticated-orcid":false,"given":"Araz Soltani","family":"Nazarloo","sequence":"first","affiliation":[{"name":"Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5981-5229","authenticated-orcid":false,"given":"Vali Rasooli","family":"Sharabiani","sequence":"additional","affiliation":[{"name":"Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-7845","authenticated-orcid":false,"given":"Yousef Abbaspour","family":"Gilandeh","sequence":"additional","affiliation":[{"name":"Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1093-6115","authenticated-orcid":false,"given":"Ebrahim","family":"Taghinezhad","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering and Technology, Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3337-0337","authenticated-orcid":false,"given":"Mariusz","family":"Szymanek","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Street G\u0142\u0119boka 28, 20-612 Lublin, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11947-016-1820-0","article-title":"Influence of rice bran wax coating on the physicochemical properties and pectin nanostructure of cherry tomatoes","volume":"10","author":"Zhang","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1093\/ajcn\/78.3.454","article-title":"Effect of orange juice intake on vitamin C concentrations and biomarkers of antioxidant status in humans","volume":"78","author":"Cano","year":"2003","journal-title":"Am. 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