{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:59:52Z","timestamp":1760241592687,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T00:00:00Z","timestamp":1527033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["JK2017007"],"award-info":[{"award-number":["JK2017007"]}],"id":[{"id":"10.13039\/501100005270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2018J01776"],"award-info":[{"award-number":["2018J01776"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China under Grant","award":["No. 61672157"],"award-info":[{"award-number":["No. 61672157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the expectation for higher quality of life increases, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food is what it says it is. A popular approach to food authentication is based on spectroscopy, which has been widely used for identifying and quantifying the chemical components of an object. This approach is non-destructive and effective but expensive. This paper presents a computer vision-based sensor system for food authentication, i.e., differentiating organic from non-organic apples. This sensor system consists of low-cost hardware and pattern recognition software. We use a flashlight to illuminate apples and capture their images through a diffraction grating. These diffraction images are then converted into a data matrix for classification by pattern recognition algorithms, including k-nearest neighbors (k-NN), support vector machine (SVM) and three partial least squares discriminant analysis (PLS-DA)- based methods. We carry out experiments on a reasonable collection of apple samples and employ a proper pre-processing, resulting in a highest classification accuracy of 94%. Our studies conclude that this sensor system has the potential to provide a viable solution to empower consumers in food authentication.<\/jats:p>","DOI":"10.3390\/s18061667","type":"journal-article","created":{"date-parts":[[2018,5,23]],"date-time":"2018-05-23T03:14:24Z","timestamp":1527045264000},"page":"1667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing\u2014A Cost-Effective Approach"],"prefix":"10.3390","volume":"18","author":[{"given":"Nanfeng","family":"Jiang","sequence":"first","affiliation":[{"name":"Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, School of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, China"}]},{"given":"Weiran","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Belfast, BT37 0QB, UK"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Belfast, BT37 0QB, UK"}]},{"given":"Gongde","family":"Guo","sequence":"additional","affiliation":[{"name":"Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, School of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, China"}]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, School of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.chemolab.2015.06.004","article-title":"Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy","volume":"146","author":"Moncayo","year":"2015","journal-title":"Chemom. 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