{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:15:11Z","timestamp":1779362111306,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,11]],"date-time":"2018-12-11T00:00:00Z","timestamp":1544486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61763049"],"award-info":[{"award-number":["61763049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Guangdong Province Support Plans for Top-Notch Youth Talents, China","award":["2016TQ03N704"],"award-info":[{"award-number":["2016TQ03N704"]}]},{"name":"the Pearl River S &amp; T Nova Program of Guangzhou, China","award":["201610010157"],"award-info":[{"award-number":["201610010157"]}]},{"name":"Planned Science and Technology Project of Guangdong Province, China","award":["2016B020202008 and 2017B010117012"],"award-info":[{"award-number":["2016B020202008 and 2017B010117012"]}]},{"name":"Planned Science and Technology Project of Guangzhou, China","award":["201704020076"],"award-info":[{"award-number":["201704020076"]}]},{"name":"the Science and Technology Plan of Applied Basic Research Programs Foundation of Yunnan Province","award":["2017FB096"],"award-info":[{"award-number":["2017FB096"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7\u20131016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher\u2019s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.<\/jats:p>","DOI":"10.3390\/s18124391","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T10:54:26Z","timestamp":1544612066000},"page":"4391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0149-4175","authenticated-orcid":false,"given":"Aimin","family":"Miao","sequence":"first","affiliation":[{"name":"College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Zhuang","sequence":"additional","affiliation":[{"name":"College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9221-6591","authenticated-orcid":false,"given":"Yu","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Chu","sequence":"additional","affiliation":[{"name":"College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoming","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s12161-015-0160-4","article-title":"Application of hyperspectral imaging to discriminate the variety of maize seeds","volume":"9","author":"Wang","year":"2016","journal-title":"Food Anal. 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