Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea.
Kaushal et al. (Fri,) studied this question.