Article
Wasserstein Distance Learns Domain Invariant
Feature Representations for Drift Compensation
of E-Nose
Yang Tao * , Chunyan Li, Zhifang Liang *, Haocheng Yang and Juan Xu
School of Communication and Information Engineering, Chongqing University of Posts and
Telecommunications, Chongqing 400065, China
* Correspondence: taoyang@cqupt.edu.cn (Y.T.); liangzf@cqupt.edu.cn (Z.L.);
Tel.: +86-139-0813-0033 (Y.T.); +86-188-7520-8445 (Z.L.)
Received: 2 August 2019; Accepted: 19 August 2019; Published: 26 August 2019
Abstract:
Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and
the corresponding pattern recognition algorithm, is used to detect the type and concentration of
gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a
variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore,
studies on the drift compensation algorithms are receiving increasing attention in the field of the
E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations
(WDLFR), is put forward for drift compensation, which is based on the domain invariant feature
representation learning. It regards a neural network as a domain discriminator to measure the
empirical Wasserstein distance between the source domain (data without drift) and target domain
(drift data). The WDLFR minimizes Wasserstein distance by optimizing the feature extractor in
an adversarial manner. The Wasserstein distance for domain adaption has good gradient and
generalization bound. Finally, the experiments are conducted on a real dataset of E-nose from
the University of California, San Diego (UCSD). The experimental results demonstrate that the
effectiveness of the proposed method outperforms all compared drift compensation methods, and the
WDLFR succeeds in significantly reducing the sensor drift.
Keywords: drift compensation; domain adaption; feature representations; electronic nose
1. Introduction
Electronic nose (E-nose) is known as machine olfaction, consisting of the gas sensor array and
corresponding pattern recognition algorithms, and is used to identify gases. Zhang et al. [
1
] and
Wang et al. [
2
] used E-nose for air quality monitoring. Yan et al. [
3
] utilized E-nose to analysis disease.
Rusinek et al. [
4
] used it for quality control of food. An increasing number of E-nose systems are being
developed into actual applications because the E-nose systems are convenient to use, fast, and cheap.
However, the sensor drift of E-nose still is a serious problem which decreases the performance of
E-nose system and is receiving more and more attention. For most chemical sensors, sensor sensitivity
may be influenced by many factors, such as environmental factors (temperature, humidity, pressure),
self-aging and poisoning, etc. The change of sensor sensitivity will result in the fluctuation of sensor
responses when the E-nose exposed to the same gas in different time, which is called the sensor drift [
5
].
In this paper, we mainly focus on the drift compensation of the sensor.
A number of methods have been applied to cope with the sensor drift of E-nose. From the
perspective of signal preprocessing methods [
6
,
7
], frequency analysis and baseline manipulation have
been adopted to compensate each sensor response. From the perspective of component correction,
Sensors 2019, 19, 3703; doi:10.3390/s19173703 www.mdpi.com/journal/sensors
