An Electronic Nose-based Method for Determination of the Storage Time of Huangshan Maofeng Tea
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Abstract:
Five grades of Huangshan Maofeng tea samples with six different storage times were analyzed by using an electronic nose. First, the original feature vectors representing the tea odor were acquired, and the first five principal components were extracted as the principal feature vectors. The principal feature vectors were used as the input of back propagation neural network (BPNN) to establish the prediction model for the storage time of Huangshan Maofeng tea (called PCA-BPNN). The test was carried out on 75 tea samples (15 samples of every grade). The results showed that for the tea at zero day of storage, the maximum prediction error was seven days and the prediction error of five samples exceeded ten days (6.67%). For the tea of 60 d of storage, the maximum prediction error was ten days, and the prediction error of four samples exceeded ten days (5.33%). For the tea of 120 d of storage, the maximum prediction error was 16 d and the prediction error of seven samples exceeded ten days (9.33%). For the tea of 180 d of storage, the maximum prediction error was 19 d and the prediction error of eight samples exceeded ten days (10.67%). For the tea of 240 d of storage, the maximum prediction error was 21 d and the prediction error of eight samples exceeded ten days (10.67%). For the tea of 300 d of storage, the maximum prediction error was 14 d and the prediction error of six samples exceeded ten days (8.00%). The feasibility of PCA-BPNN prediction model to determine the storage time of Huangshan Maofeng tea was verified. Moreover, the performance of PCA-BPNN prediction model was better than that of BPNN prediction model using the original feature vectors as the input.
