Bionic electronic nose based on MOS sensors array and machine learning algorithms used for wne properties detection

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with differe
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.
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Metadaten
Author:Huixiang Liu, Qing Li, Bin Yan, Lei Zhang, Yu Gu
URN:urn:nbn:de:hebis:30:3-486049
DOI:http://dx.doi.org/10.3390/s19010045
ISSN:1424-8220
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=30583545
Parent Title (English):Sensors
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/12/22
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/01/10
Tag:machine learning; portable electronic nose; support vector machine; wine
Volume:19
Issue:Art. 45
Pagenumber:11
First Page:1
Last Page:11
Note:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Institutes:Biochemie und Chemie
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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