TY - JOUR
T1 - Channel Attention Convolutional Neural Network for Chinese Baijiu Detection with E-Nose
AU - Zhang, Shanshan
AU - Cheng, Yu
AU - Luo, Dehan
AU - He, Jiafeng
AU - Wong, Angus K.Y.
AU - Hung, Kevin
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2021/7/15
Y1 - 2021/7/15
N2 - Electronic nose (E-nose) plays an important role in the detection of Chinese baijiu, which is an alcoholic beverage of high reputation. However, traditional e-nose data processing relies on manual selection of features with complicated preprocessing steps. The production process of baijiu with the same flavor is similar, but due to the limited number of sensors, it is not easy to quickly extract specific features with e-nose. To overcome this shortcoming, we propose a novel method based on Channel Attention Convolutional Neural Network (CA-CNN) for authenticity identification of Chinese baijiu. The underlying channel attention module analyzes the dependencies between channels and learning weights in order to improve the detection accuracy. In particular, we evaluated the comprehensive performance of the system by comparing it with traditional machine learning methods. The prediction accuracy of the CA-CNN (98.53%) is better than the back-propagation artificial neural network (BP-ANN) (90.83%), the support vector machine (SVM) (84.5%), and the random forest (RF) (86.867%). The experiment results show that the CA-CNN has reasonable reliability, stability and good prediction performance in the quality classification of Chinese baijiu. It provides an effective reference method for the standardization of baijiu quality inspection.
AB - Electronic nose (E-nose) plays an important role in the detection of Chinese baijiu, which is an alcoholic beverage of high reputation. However, traditional e-nose data processing relies on manual selection of features with complicated preprocessing steps. The production process of baijiu with the same flavor is similar, but due to the limited number of sensors, it is not easy to quickly extract specific features with e-nose. To overcome this shortcoming, we propose a novel method based on Channel Attention Convolutional Neural Network (CA-CNN) for authenticity identification of Chinese baijiu. The underlying channel attention module analyzes the dependencies between channels and learning weights in order to improve the detection accuracy. In particular, we evaluated the comprehensive performance of the system by comparing it with traditional machine learning methods. The prediction accuracy of the CA-CNN (98.53%) is better than the back-propagation artificial neural network (BP-ANN) (90.83%), the support vector machine (SVM) (84.5%), and the random forest (RF) (86.867%). The experiment results show that the CA-CNN has reasonable reliability, stability and good prediction performance in the quality classification of Chinese baijiu. It provides an effective reference method for the standardization of baijiu quality inspection.
KW - Chinese baijiu
KW - E-nose
KW - channel attention
KW - convolutional neural network
KW - detection
UR - http://www.scopus.com/inward/record.url?scp=85105025952&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2021.3075703
DO - 10.1109/JSEN.2021.3075703
M3 - Article
AN - SCOPUS:85105025952
SN - 1530-437X
VL - 21
SP - 16170
EP - 16182
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 14
M1 - 9416478
ER -