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    Abstract
2017 (Vol. 13, Issue: 4)
Research Article

Identification of Vinegar Flavor using Electronic Nose

Hong-Biao Zhou
Faculty of Automation, Huaiyin Institute of Technology, Huaiían, China
 

DOI: 10.19026/ajfst.13.4442
Submitted Accepted Published
July ‎12, ‎2016 April 27, 2017 April 25, 2017

  How to Cite this Article:

Hong-Biao Zhou, 2017. Identification of Vinegar Flavor using Electronic Nose.  Research Journal of Applied Sciences, Engineering and Technology, 13(4): 154-160.

DOI: 10.19026/ajfst.13.4442

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=AJFST&doi=ajfst.13.4442

Abstract:


As one of the most popular condiments, vinegarís 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 vinegarís 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 odorís 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. Itís a feasible way to discriminate different flavor vinegar with the self-developed electronic nose.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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© The Author(s) 2017

ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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