{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:41:50Z","timestamp":1773416510377,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Israeli Authority of Innovation","award":["73815"],"award-info":[{"award-number":["73815"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The evaluation of an oil\u2019s oxidation status during industrial production is highly important for monitoring the oil\u2019s purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil\u2019s material characterization. Towards this goal, a 1H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a 1H LF-NMR relaxation curve, and its reconstruction into T1 and T2 spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T2 relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T2 signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.<\/jats:p>","DOI":"10.3390\/s23042125","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"2125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status"],"prefix":"10.3390","volume":"23","author":[{"given":"Tatiana","family":"Osheter","sequence":"first","affiliation":[{"name":"Phyto-Lipid Biotech Lab (PLBL), Department of Biotechnology Engineering, Ben Gurion University of the Negev, Beer Sheva 8499000, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8840-4648","authenticated-orcid":false,"given":"Salvatore","family":"Campisi Pinto","sequence":"additional","affiliation":[{"name":"Phyto-Lipid Biotech Lab (PLBL), Department of Biotechnology Engineering, Ben Gurion University of the Negev, Beer Sheva 8499000, Israel"}]},{"given":"Cristian","family":"Randieri","sequence":"additional","affiliation":[{"name":"eCampus University, Via Isimbardi, 10, 22060 Novedrate, Italy"}]},{"given":"Andrea","family":"Perrotta","sequence":"additional","affiliation":[{"name":"eCampus University, Via Isimbardi, 10, 22060 Novedrate, Italy"}]},{"given":"Charles","family":"Linder","sequence":"additional","affiliation":[{"name":"Phyto-Lipid Biotech Lab (PLBL), Department of Biotechnology Engineering, Ben Gurion University of the Negev, Beer Sheva 8499000, Israel"}]},{"given":"Zeev","family":"Weisman","sequence":"additional","affiliation":[{"name":"Phyto-Lipid Biotech Lab (PLBL), Department of Biotechnology Engineering, Ben Gurion University of the Negev, Beer Sheva 8499000, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e539426","DOI":"10.1155\/2012\/539426","article-title":"Health Implications of High Dietary Omega-6 Polyunsaturated Fatty Acids","volume":"2012","author":"Patterson","year":"2012","journal-title":"J. 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