Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining m...

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Main Authors: Hong Men, Yan Shi, Songlin Fu, Yanan Jiao, Yu Qiao, Jingjing Liu
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/17/7/1656
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spelling doaj-d25da0cb575f4b6ba509da2a7d4cc1cd2020-11-24T22:24:01ZengMDPI AGSensors1424-82202017-07-01177165610.3390/s17071656s17071656Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-NoseHong Men0Yan Shi1Songlin Fu2Yanan Jiao3Yu Qiao4Jingjing Liu5College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaMulti-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.https://www.mdpi.com/1424-8220/17/7/1656e-tonguee-nosedata fusionfeature miningvariable accumulationbeer
collection DOAJ
language English
format Article
sources DOAJ
author Hong Men
Yan Shi
Songlin Fu
Yanan Jiao
Yu Qiao
Jingjing Liu
spellingShingle Hong Men
Yan Shi
Songlin Fu
Yanan Jiao
Yu Qiao
Jingjing Liu
Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
Sensors
e-tongue
e-nose
data fusion
feature mining
variable accumulation
beer
author_facet Hong Men
Yan Shi
Songlin Fu
Yanan Jiao
Yu Qiao
Jingjing Liu
author_sort Hong Men
title Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
title_short Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
title_full Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
title_fullStr Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
title_full_unstemmed Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose
title_sort mining feature of data fusion in the classification of beer flavor information using e-tongue and e-nose
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-07-01
description Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.
topic e-tongue
e-nose
data fusion
feature mining
variable accumulation
beer
url https://www.mdpi.com/1424-8220/17/7/1656
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