Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification

Sensors
Wei ZhaoPeifeng Qi

Abstract

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options. The main advantage of SSAE is that it can automatically learn features from the original sensor data without the steps of preprocessing and feature extraction; which can greatly simplify data processing procedures for e-noses. To identify different brands of Chinese liquors; an SSAE based multi-layer back propagation neural network (BPNN) is constructed. Seven kinds of strong-flavor Chinese liquors were selected for a self-designed e-nose to test the performance of the proposed method. Experimental results show that the proposed method outperforms the traditional methods.

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Jul 29, 2006·Science·G E Hinton, R R Salakhutdinov
Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Clément FarabetYann Lecun
Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hoo-Chang ShinMartin O Leach

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Citations

Jan 25, 2019·Oncology Letters·Xue Hu, Zebo Yu

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

DeepLearn Toolbox
KECA

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