ArticleSensors (Basel, Switzerland)2017
Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.Microorganisms · 2023Review
- A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose.Foods (Basel, Switzerland) · 2023Article
- Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors.Sensors (Basel, Switzerland) · 2022Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM.Sensors (Basel, Switzerland) · 2019Article
- Diagnosis of mesothelioma with deep learning.Oncology letters · 2019Article
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Authors and funding
4 authors.
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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.
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