Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods
文献类型： 外文期刊
作者： Jiang, Qinghai 1 ; Mei, Song 1 ; Zhan, Caixue 1 ; Ren, Caihong 1 ; Song, Zhiyu 1 ; Wang, Shengpeng 2 ;
作者机构： 1.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing, Peoples R China
2.Hubei Acad Agr Sci, Inst Fruit & Tea, Wuhan, Peoples R China
关键词： fresh tea leaves; altitude; near infrared spectroscopy; backward interval partial least squares; least squares support vector machine
期刊名称：FOOD SCIENCE AND TECHNOLOGY （ 影响因子：2.602； 五年影响因子：2.968 ）
ISSN： 0101-2061
年卷期： 2022 年 43 卷
页码：
收录情况： SCI
摘要： Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.2-5091.2 cm-1, 5368.9-5638.8 cm-1, 6190.4-6460.4 cm -1, 7011.9-7281.9 cm-1, 8924.9-9191.1 cm-1 and 9734.9-10000 cm-1. The cumulative contribution rate of the first three principal components was 99.92%. The root mean square error of the cross validation and the determination coefficient of the calibration set model were 0.027 and 0.973, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.034 and 0.968, respectively. The discrimination accuracy in prediction set was 100%. The results showed NIRS combined with LS-SVM can realize fast and nondestructive discrimination of fresh tea leaves at different altitudes.
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