Search

The quality of crush–tear–curl black tea (CTC-BT) varies greatly by geographic origin. Origin traceability is crucial for consumer interest protection, market order regulation, and food safety monitoring. This paper proposes a fast and accurate method for qualitative discrimination of CTC-BT origins and quantitative detection of its key taste-presenting substances. The method involves a simple colorimetric sensor array and ultraviolet–visible spectroscopy. The effects of various variable screening methods on modeling results were compared. A particle swarm optimization–based support vector machine achieved the highest performance in qualitative discrimination, with a correct classification rate of 99.48%. Based on their origin-distinguishing contributions and dose-over-thresholds, seven key taste-presenting substances were screened, namely, theaflavin, caffine, vitexin-2- O -rhamnoside, rutin, epigallocatechin gallate, epicatechin gallate, gallic acid. A least squares-support vector regression model achieved accurate quantification of the seven aforementioned compounds (square root of determination coefficient of prediction >0.9698, residual prediction deviation >2). [Display omitted] • A simple method for CTC black tea origin tracing and taste evaluation was developed. • Stable and simplified solution phase-colorimetric sensing array was built. • SG-PSO-SVM model achieved best CCR of 99.48% in origin discrimination. • Taste compounds were screened by origin-distinguishing contributions and thresholds. • Accurate quantification (Rp > 0.9698, RPD>2) of 7 taste compounds were achieved. [ABSTRACT FROM AUTHOR]

Crush–tear–curl (CTC) black tea is a popular beverage, owing to its unique taste characteristics and health benefits. However, differences in the taste quality and chemical profiles of CTC black tea from different geographical regions remain unclear. In this study, 28 CTC black tea samples were collected from six geographical regions and analyzed using electronic tongue and ultrahigh performance liquid chromatography‐Orbitrap‐mass spectroscopy. The e‐tongue analysis indicated that each region's CTC black tea has its own relatively prominent taste characteristics: Sri Lanka (more umami and astringent), North India (more umami), China (more sweetness and astringent), South India (moderate umami and sweetness), and Kenya (moderate umami and astringent). Based on multivariate statistical analysis, 78 metabolites were tentatively identified and used as potential markers for CTC black tea of different origins, mainly including amino acids, flavone/flavonol glycosides, and pigments. Different metabolites, which contributed to the taste characteristics of CTC black tea, were clarified by partial least squares regression correlation analysis. Our findings may serve as useful references for future studies on origin traceability and quality characteristic determination of CTC black teas. Practical Application: This study provides useful references for future studies on the origin traceability and taste characteristic determination of CTC black teas from different geographical regions. [ABSTRACT FROM AUTHOR]

The current method for processing bud yellow tea usually results in astringent taste that is not sufficiently yellowing. Therefore, two factors in the production phase that affect tea quality were studied: number of times the tea leaves undergo the yellowing process and predrying of leaves to reduce their water content before a second yellowing process. We then evaluated the colour and nonvolatile compounds of the produced tea. Results revealed that gallated catechins and the polyphenol–amino acid ratio decreased significantly, and the colour of the tea changed from green to yellow after two yellowing processes. Predrying leaves to a lower water content resulted in the accumulation of free amino acids and soluble sugars, and an increase in the brightness and yellow colour of tea infusion. Bud yellow tea with the highest taste evaluation score and yellowness was obtained when the leave samples underwent two yellowing processes and the water content in the leaves was 40%. [ABSTRACT FROM AUTHOR]

• Estimation of Congou black tea quality by an artificial lipid membrane taste sensor. • Optimizing taste features using ant colony optimization. • Multivariate analyses of quality categories and taste characteristics are performed. • The ACO-LS-SVM model achieves the desired predictive performance. The taste attribute is an important indicator to estimate the quality and rank of black tea. It is crucial to prevent fraud and avoid economic losses. This study creatively proposed the assessment of Congou black tea quality based on an electronic tongue system combined with the ant colony optimization (ACO) algorithm and stoichiometry. Firstly, the electronic tongue could effectively present the taste quality of black tea, capturing characteristic potential signals from 700 samples of seven different qualities and converting the signals into nine relative characteristic taste values. Then, the taste indicators from different artificial lipid membrane sensors were optimized using the ACO method. Finally, the discriminant models based on the extreme learning machine, support vector machine, partial least-squares discriminant analysis, and least squares-support vector machine (LS-SVM) were developed using the optimized taste characteristics for the assessment of black tea quality. Results indicated that the LS-SVM model, which was created using the five taste features, could obtain better predictive outcomes based on the generalization performance of the model. In the prediction set, the correct discriminant rate was 99.14%. The overall results demonstrate that the electronic tongue sensor array has potential application prospects for the assessment of Congou black tea products in the actual production process. [ABSTRACT FROM AUTHOR]