Evaluating taste-related attributes of black tea by micro-NIRS. (February 2021)

• Record Type: Journal Article
• Authors: Wang, Yu-Jie; Li, Tie-Han; Li, Lu-Qing; Ning, Jing-Ming; Zhang, Zheng-Zhu
• Is Part Of: Journal of food engineering. Volume 290(2021)
• Publication Date: 2021-02
• Subjects: Micro-near-infrared spectrometer -- Black tea -- Taste attributes -- Competitive adaptive reweighted sampling; CTC crush, tear and curl -- EGCG epigallocatechin gallate -- HPLC high-performance liquid chromatography -- NIRS near-infrared spectroscopy -- SPA successive projection algorithm -- CARS competitive adaptive reweighted sampling -- PLSR partial least squares regression -- MLR multiple linear regression -- LVs latent variables -- Rc correlation coefficient in calibration set -- Rp correlation coefficient in prediction set -- RMSEC root mean square errors in calibration set -- RMSEP root mean square errors in prediction set -- RPDcv residual predictive deviation in cross-validation -- RPDp residual predictive deviation in prediction; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2020.110181
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Tea taste assessments generally rely on panel sensory evaluation, which often yield inconsistent results. Therefore, the rapid and nondestructive assessment of the taste attributes of tea is important for its quality evaluation. This study assessed black tea taste attributes using a novel low-cost evaluation method that employed a smartphone-connected micro-near-infrared (micro-NIR) spectrometer. Bitterness and astringency intensity were evaluated by a trained panel, and caffeine and epigallocatechin gallate (EGCG) contents were analyzed using high-performance liquid chromatography. Partial least squares regression and multiple linear regression models were established on characteristic wavelengths selected using the successive projection algorithm and competitive adaptive reweighted sampling (CARS), respectively. The optimal prediction models obtained after conducting CARS selection yielded satisfactory results, with residual predictive deviation of 3.07, 2.28, 3.29, and 2.91 for bitterness score, astringency score, caffeine, and EGCG content, respectively. The results proved that micro-NIR spectrometers can be used to predict the taste attributes of black tea, providing a new method for the quality assessment black tea. Highlights: Miniature spectrometer was used to evaluate taste attributes of black tea. A total of 56 black tea samples from five countries were analyzed and studied. Simplified models showed high prediction performance for all taste attributes. Our results provide templates for the in-situ and online monitoring of tea quality.