{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:02:59Z","timestamp":1773511379552,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["CX(21)3058"],"award-info":[{"award-number":["CX(21)3058"]}]},{"name":"the National University Student Entrepreneurship Practicing Program of China","award":["202110307004S"],"award-info":[{"award-number":["202110307004S"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA\u2013KNN-SVM classifier was 96.45%, and the LDA\u2013KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.<\/jats:p>","DOI":"10.3390\/s22082997","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"2997","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8074-7555","authenticated-orcid":false,"given":"Xiuguo","family":"Zou","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Chenyang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Manman","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Qiaomu","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-0285","authenticated-orcid":false,"given":"Yingying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Shikai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Yungang","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1940-1301","authenticated-orcid":false,"given":"Jiawei","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University College London, London WC1E 7JE, UK"}]},{"given":"Wentian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia"}]},{"given":"Steven W.","family":"Su","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1016\/j.compag.2020.105634","article-title":"Multi-class Fruit-on-plant Detection for Apple in SNAP System Using Faster R-CNN","volume":"176","author":"Gao","year":"2020","journal-title":"Comput. 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