{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:04:40Z","timestamp":1771189480962,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Rural Development Administration, Korea","award":["PJ012216"],"award-info":[{"award-number":["PJ012216"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400\u20131000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.<\/jats:p>","DOI":"10.3390\/s21092899","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:25:10Z","timestamp":1619040310000},"page":"2899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2140-5333","authenticated-orcid":false,"given":"Youngwook","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"given":"Giyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-4367","authenticated-orcid":false,"given":"Jongguk","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-6282","authenticated-orcid":false,"given":"Ahyeong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"given":"Balgeum","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"given":"Jaekyung","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea"}]},{"given":"Changyeun","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Gangwon-do, Korea"},{"name":"Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea"}]},{"given":"Moon S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, USDA, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1177\/1082013207079610","article-title":"Physical, Physiological and Microbial Deterioration of Minimally Fresh Processed Fruits and Vegetables","volume":"13","author":"Artes","year":"2007","journal-title":"Food Sci. 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