TY  - JOUR
A1  - Liu, Huixiang
A1  - Li, Qing
A1  - Yan, Bin
A1  - Zhang, Lei
A1  - Gu, Yu
T1  - Bionic electronic nose based on MOS sensors array and machine learning algorithms used for wne properties detection
T2  - Sensors
N2  - 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.
KW  - portable electronic nose
KW  - wine
KW  - machine learning
KW  - support vector machine
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/48604
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-486049
SN  - 1424-8220
N1  - 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).
VL  - 19
IS  - Art. 45
SP  - 1
EP  - 11
PB  - MDPI
CY  - Basel
ER  -