Abstract

As one of the most popular condiments, vinegar’s quality has been widely concerned. Discrimination of vinegar which is composed of a complex mixture of very similar compositions by chemical analysis means is a remaining challenge. In order to explore possibility of identification of vinegar’s quality by electrochemical methods, we have developed an electronic nose with gas sensor array of different selectivity composed of eight sensors (TGS813, TGS822, TGS826, TGS2600, TGS2602, TGS2610, TGS2611 and TGS2620). The experiment process is automatically measured by a virtual testing application platform with LabVIEW, which can realize data acquisition, data storage, data processing and so on. The odor’s fingerprint of five different flavor vinegar, including white vinegar, mature vinegar, rice vinegar, balsamic vinegar and apple vinegar, are collected using the electronic nose. Multivariate statistical analyses, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), are employed to analyze all of these samples. Meanwhile, the multilayer perceptron (MLP) recognition model is established. The results show that both PCA and LDA can distinguish different flavor samples and the MLP has achieved higher recognition accuracy. It’s a feasible way to discriminate different flavor vinegar with the self-developed electronic nose.

Keywords:

Electronic nose, linear discriminant analysis, multilayer perceptron, principal component analysis, vinegar,

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Competing interests

The authors have no competing interests.

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