{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:02:47Z","timestamp":1778346167395,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Training Programs of Innovation and Entrepreneurship for Undergraduates","award":["201810336031"],"award-info":[{"award-number":["201810336031"]}]},{"name":"the Joint Funds of National Natural Science Foundation of China","award":["U1609218"],"award-info":[{"award-number":["U1609218"]}]},{"name":"the Joint Funds of National Natural Science Foundation of China","award":["61705056"],"award-info":[{"award-number":["61705056"]}]},{"name":"Public Projects of Zhejiang Province","award":["LGF19H180005"],"award-info":[{"award-number":["LGF19H180005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.<\/jats:p>","DOI":"10.3390\/s19214687","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T11:26:13Z","timestamp":1572261973000},"page":"4687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1361-0663","authenticated-orcid":false,"given":"Hongze","family":"Lin","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejian","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huajin","family":"Lu","sequence":"additional","affiliation":[{"name":"Southern Zhejiang key Laboratory of Crop Breeding, Wenzhou Academy of Agricultural Sciences, Wenzhou 325006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Wenzhou Specialty Station, Wenzhou 325006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2455-9226","authenticated-orcid":false,"given":"Fengnong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaihua","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dazhou","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.saa.2012.10.052","article-title":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques","volume":"103","author":"Jurado","year":"2013","journal-title":"Spectrochim. 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