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Open AccessArticle

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

by Zhiqi Hong 1 and Yong He 2,3,*
1
Agricultural Experiment Station, Zhejiang University, Hangzhou 310058, China
2
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(3), 1173; https://doi.org/10.3390/app10031173
Received: 8 January 2020 / Revised: 3 February 2020 / Accepted: 5 February 2020 / Published: 10 February 2020
(This article belongs to the Special Issue Hyperspectral Imaging, Methods and Applications)
Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification. View Full-Text
Keywords: hyperspectral imaging; Longjing tea; geographical origins; support vector machine; prediction maps hyperspectral imaging; Longjing tea; geographical origins; support vector machine; prediction maps
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Hong, Z.; He, Y. Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods. Appl. Sci. 2020, 10, 1173.

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