Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

An electronic nose (EN) is a bionic system that relies on an array of gas sensors for effective odor recognition. Since the gas-sensor drift would depress the EN performance, we proposed an adaptive Domain based subspace learning method considering both Maximizing label feature Dependency and Minimizing feature Redundancy (DMDMR) to address the EN based drift issue. Considering the inconsistent data distribution caused by drift, the proposed method learns a time-varying common subspace with similar distribution for both regular and recent drifted EN responses. In order to preserve useful classification and topological structure information simultaneously, the Hilbert-Schmidt independence criterion (HSIC) has been adopted to measure the dependence between features and labels while the feature redundancy is reduced according to the PCA criterion. An EN drift dataset acquired from a 16-gas-sensor EN system over 36 months was adopted in the experiments. To verify the effectiveness of the proposed method, we used the variations of both first and second order statistics to measure the movement of data distribution under drift condition. The results show that the data distribution in the subspace learned by DMDMR has stronger stability than the one in original space. Furthermore, the positive effects of DMDMR have been exhibited versus other state-of-the-art methods in recognition. The performance of DMDMR paradigm has demonstrated obvious superiority to other paradigms. These results prove that the proposed subspace learning approach is suitable for EN based drift counteraction and can be successfully implemented.

Gas sensors are the key components of an electronic nose (E-nose) in violated odour analysis. Gas-sensor drift is a kind of physical change on a sensor surface once an E-nose works. The perturbation of gas-sensor responses caused by drift would deteriorate the performance of the E-nose system over time. In this study, we intend to explore a suitable approach to deal with the drift effect in an online situation. Considering that the conventional drift calibration is difficult to implement online, we use active learning (AL) to provide reliable labels for online instances. Common AL learning methods tend to select and label instances with low confidence or massive information. Although this action clarifies the ambiguity near the classification boundary, it is inadequate under the influence of gas-sensor drift. We still need the samples away from the classification plane to represent drift variations comprehensively in the entire data space. Thus, a novel drift counteraction method named AL on adaptive confidence rule (AL-ACR) is proposed to deal with online drift data dynamically. By contrast with conventional AL methods selecting instances near the classification boundary of a certain category, AL-ACR collects instances distributed evenly in different categories. This action implements on an adjustable rule according to the outputs of classifiers. Compared with other reference methods, we adopt two drift databases of E-noses to evaluate the performance of the proposed method. The experimental results indicate that the AL-ACR reaches higher accuracy than references on two E-nose databases, respectively. Furthermore, the impact of the labelling number is discussed to show the trend of performance for the AL-type methods. Additionally, we define the labelling efficiency index (LEI) to assess the contribution of certain labelling numerically. According to the results of LEI, we believe AL-ACR can achieve the best effect with the lowest cost among the AL-type methods in this work.