NDLI: Development of a Rapid Discrimination Tool for Luzhou-flavor Pit Mud Classification by the Kohonen Artificial Neural Network Model

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Development of a Rapid Discrimination Tool for Luzhou-flavor Pit Mud Classification by the Kohonen Artificial Neural Network Model

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Development of a Rapid Discrimination Tool for Luzhou-flavor Pit Mud Classification by the Kohonen Artificial Neural Network Model

Content Provider	Springer Nature Link
Author	Zheng, Jia Liang, Ru Wu, Chongde Huang, Jun Zhou, Rongqing Liao, Xuepin
Copyright Year	2014
Abstract	Understanding the volatile composition of pit mud (PM) samples and discriminating these samples has become a highly necessary task, owing to the fact that volatile profiling of PM can significantly affect Chinese Luzhou-flavor liquor quality. In this study, the volatile constitutions of 13 Luzhou-flavor liquor PM samples from four typical producing regions were investigated by gas chromatography–mass spectrometry (GC-MS). Owing to their high concentrations, compounds such as ethyl hexanoate, butyric acid, hexanoic acid, ethyl pentadecanoate, ethyl palmitate, ethyl (9E)-9-octadecenoate, ethyl (9E,12E)-9,12-octadecadienoate, and palmitic acid were considered to be predominant volatiles. A promising artificial neural network model, the Kohonen self-organizing map (SOM), was applied to rapidly discriminate the PM samples in terms of differences based on the quantitative information of volatile compounds. After Kohonen SOM training, 13 distinct clusters, corresponding to PM samples, were clearly visualized on a uniform distance matrix (U-matrix). The influence of volatile compounds on the classification of the PM could be displayed using component panels, which can give quantitative insight. GC-MS coupled with the Kohonen SOM model not only presented the volatile constitution of PM but also provided promising information for discrimination between different PM samples, even in other fermented foods.
Starting Page	1734
Ending Page	1738
Page Count	5
File Format	PDF
ISSN	19369751
Journal	Food Analytical Methods
Volume Number	8
Issue Number	7
e-ISSN	1936976X
Language	English
Publisher	Springer US
Publisher Date	2014-12-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Luzhou-flavor liquor Kohonen SOM Volatile compounds Pit mud Rapid discrimination Food Science Chemistry/Food Science Microbiology Analytical Chemistry
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Safety Research Safety, Risk, Reliability and Quality Applied Microbiology and Biotechnology Food Science

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