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Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics

Authors :
Yujie Wang
Luqing Li
Yuyu Chen
Junlan Huang
Ze Xu
Ying Liu
Yingfu Zhong
Chengye Lu
Qingqing Cui
Jingming Ning
Menghui Li
Source :
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 267:120537
Publication Year :
2022
Publisher :
Elsevier BV, 2022.

Abstract

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.

Details

ISSN :
13861425
Volume :
267
Database :
OpenAIRE
Journal :
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Accession number :
edsair.doi.dedup.....0f4acc3757ff61e6951cea8006969665