Next Article in Journal
A Wavefront Division Polarimeter for the Measurements of Solute Concentrations in Solutions
Next Article in Special Issue
Biomimetic Sensors for the Senses: Towards Better Understanding of Taste and Odor Sensation
Previous Article in Journal / Special Issue
Chemical Selectivity and Sensitivity of a 16-Channel Electronic Nose for Trace Vapour Detection
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2855; https://doi.org/10.3390/s17122855

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

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
*
Authors to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 8 December 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
View Full-Text   |   Download PDF [6990 KB, uploaded 8 December 2017]   |  

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. View Full-Text
Keywords: stacked sparse auto-encoders; electronic nose; deep learning; Chinese liquors classification stacked sparse auto-encoders; electronic nose; deep learning; Chinese liquors classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhao, W.; Meng, Q.-H.; Zeng, M.; Qi, P.-F. Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification. Sensors 2017, 17, 2855.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top