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Sensors 2017, 17(7), 1656; https://doi.org/10.3390/s17071656

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

College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
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Received: 31 May 2017 / Revised: 14 July 2017 / Accepted: 14 July 2017 / Published: 19 July 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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Abstract

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. View Full-Text
Keywords: e-tongue; e-nose; data fusion; feature mining; variable accumulation; beer e-tongue; e-nose; data fusion; feature mining; variable accumulation; beer
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Men, H.; Shi, Y.; Fu, S.; Jiao, Y.; Qiao, Y.; Liu, J. Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose. Sensors 2017, 17, 1656.

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