Characterization of Chinese vinegars by electronic nose

In this paper, 17 commercial Chinese vinegars, acetic acid and 5% diluted acetic acid were analyzed by an electronic nose containing nine nano ZnO thick film gas sensors, which are doped by 5 wt.% and 10 wt.% TiO2, 5 and 10 wt.% MnO2, 1 wt.% V2O5, 5 wt.% Bi2O3, 0.6 and 2.4 wt.% Ag, and 5 wt.% W, respectively. Principal component analysis (PCA) and cluster analysis (CA) were employed to investigate the presence of classes inside the sample population. It was shown that characterizing the Chinese vinegars by the electronic nose was highly related to their type, raw materials, total acidity, fermentation method and production area and all these influencing factors were not independent. The CA results indicated that the type and fermentation method were more effective than the other influencing factors when the vinegars were analyzed by the electronic nose. Finally, the data colleted by the electronic nose were applied to the learning vector quantization (LVQ) neural network performing the role of recognition and classification of the vinegars. The accuracy in terms of predicting tested vinegar measurements was 72.1%, 76.5%, 77.9%, 94.1% and 82.4% according to their type, raw materials, total acidity, fermentation method and production area, respectively. This work was the first step to establish a gas-sensing fingerprint database of Chinese vinegars and develop a commercial electronic nose on the Chinese vinegars quality control.