ISSN 0914-4935
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Identification of Xinyang Maojian Tea Taste using Electronic Tongue

Hongmei Zhang, Guangyu Zou, Xinping Liu, Yanzhong Xiao, and Wanzhang Wang

(Received December 21, 2018; Accepted March 6, 2019)

Keywords: Xinyang Maojian tea, aftertaste, principal component analysis, linear discriminant analysis, support vector machine

The taste of Xinyang Maojian tea was tested using an electronic tongue to conduct a correlation analysis between the taste of tea and the quality of tea. It was determined that the astringent aftertaste had a significant relationship with the quality of tea (p < 0.01). The aftertaste properties of tea were evaluated before and after bitterness, astringency, and richness (fresh aftertaste) aftertastes were added to six types of tea by comparing principal component analysis and linear discriminant analysis. The principal component analysis and linear discriminant analysis results were affected by the aftertaste attributes of tea soup. After the addition of aftertaste, the classification effect of principal component analysis was reduced, and the classification effect of linear discriminant analysis was improved. After introducing aftertaste, the linear discriminant analysis had the best effect on tea differentiation. Finally, a support vector machine model was established for tea grade evaluation based on the tea aftertaste and basic taste attributes. The values of the support vector machine penalty function c and the kernel function search radius g were optimized by the grid search and particle swarm optimization methods, respectively, to compare the classification performance of the two optimization methods. The grid search method was used to analyze the global classification effect, which had a heavy calculation burden and was unable to achieve further optimization near the optimum point. The particle swarm optimization method had the iterative characteristics and it was easier to determine the optimal point. The classification accuracies of the training set and test set of the grid search method were 86.11% and 87.5%, respectively, and the accuracies of training set and test set of the particle swarm optimization method both reached 100%, indicating the superiority of the particle swarm optimization method over the grid search method. In conclusion, the effectiveness of an electronic tongue for distinguishing the quality of Xinyang Maojian tea was demonstrated.

Corresponding author: Hongmei Zhang




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