Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. (30th May 2021)

• Record Type: Journal Article
• Authors: Wang, Yujie; Li, Menghui; Li, Luqing; Ning, Jingming; Zhang, Zhengzhu
• Is Part Of: Food chemistry. Volume 345(2021)
• Publication Date: 2021-05-30
• Subjects: NIR near-infrared -- NIRS near-infrared spectroscopy -- PLSR partial least squares regression -- PLS–DA partial least squares–discriminant analysis -- RBF–SVM radial basis function–support vector machine -- IVSO iteratively variable subset optimization -- CTC crush, tear, and curl -- GA genetic algorithms -- SPA successive projection algorithms -- CARS competitive adaptive reweighted sampling -- VCPA variable combination population analysis -- iVISSA interval variable iterative space shrinkage approach -- SG Savitzky–Golay -- 1D first derivative -- De detrending -- SNV standard normal variable -- LVs latent variables -- CCR correct classification rate -- Rc correlation coefficient in calibration set -- RMSECV root mean square error of cross-validation -- Rp correlation coefficient in prediction set -- RMSEP root mean standard error of prediction -- RPD residual predictive deviation -- SD standard deviation -- EDF exponential declining function -- BMS binary matrix sampling -- WBMS weighted binary matrix sampling -- LDA linear discriminant analysis; Micro NIR spectrometer -- Tea type -- Caffeine -- Catechins -- Theanine -- Chemometrics; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.128816
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Pocket-sized NIRS was used to evaluate tea quality qualitatively and quantitatively. Accurate tea type discrimination and component prediction models were obtained. Novel IVSO can improve the accuracies of catechin, caffeine, and theanine models. It provided a green and low-cost method for the in-situ assessment of tea quality. Abstract: Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function–support vector machine (RBF–SVM) were used. The RBF–SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO–PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.