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Sensors (Basel). 2018 Dec 22;19(1). pii: E45. doi: 10.3390/s19010045.

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection.

Author information

1
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. liuhuixiang@xs.ustb.edu.cn.
2
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China. liqing@ies.ustb.edu.cn.
3
COFCO Huaxia Greatwall Wine Co., Ltd. No. 555, Changli 066600, China. ms.yan@163.com.
4
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China. zhanglei@hebut.edu.cn.
5
Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China. guyu@mail.buct.edu.cn.
6
Department of Chemistry, Institute of Inorganic and Analytical Chemisty, Goethe-University, 60438 Frankfurt, Germany. guyu@mail.buct.edu.cn.

Abstract

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.

KEYWORDS:

machine learning; portable electronic nose; support vector machine; wine

PMID:
30583545
DOI:
10.3390/s19010045
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