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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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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