Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis

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.

Automated summary

This study proposed a method for assessing the quality of Congou black tea using an electronic tongue system combined with optimization algorithms. The optimized taste characteristics led to better predictive outcomes, with the LS-SVM model achieving a correct discriminant rate of 99.14% in the prediction set, demonstrating the potential application prospects of electronic tongue sensor arrays for evaluating black tea products in the production process.