Tea quality evaluation by applying E-nose combined with chemometrics methods.

Tea is one of the most popular beverage with distinct flavor consumed worldwide. It is of significance to establish evaluation method for tea quality controlling. In this work, electronic nose (E-nose) was applied to assess tea quality grades by detecting the volatile components of tea leaves and tea infusion samples. The "35th s value", "70th s value" and "average differential value" were extracted as features from E-nose responding signals. Three data reduction methods including principle component analysis (PCA), multi-dimensional scaling (MDS) and linear discriminant analysis (LDA) were introduced to improve the efficiency of E-nose analysis. Logistic regression (LR) and support vector machine (SVM) were applied to set up qualitative classification models. The results indicated that LDA outperformed original data, PCA and MDS in both LR and SVM models. SVM had an advantage over LR in developing classification models. The classification accuracy of SVM based on the data processed by LDA for tea infusion samples was 100%. Quantitative analysis was conducted to predict the contents of volatile compounds in tea samples based on E-nose signals. The prediction results of SVM based on the data processed by LDA for linalool (training set: R2 = 0.9523; testing set: R2 = 0.9343), nonanal (training set: R2 = 0.9617; testing set: R2 = 0.8980) and geraniol (training set: R2 = 0.9576; testing set: R2 = 0.9315) were satisfactory. The research manifested the feasibility of E-nose for qualitatively and quantitatively analyzing tea quality grades. © Association of Food Scientists & Technologists (India) 2020.