Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Automated summary

The taste of tea is crucial in evaluating its quality and pricing. This study utilized miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors to objectively assess the taste quality of black tea. Using data fusion strategies and chemometric tools, the combination of NIR spectroscopy and ET sensor signals effectively predicted the multiple grades of black tea. The ACO-SVM model achieved the highest classification accuracy of 93.56%. These results demonstrate the feasibility of qualitatively distinguishing black tea grades and categories using NIR spectroscopy and ET techniques.