# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 1 Introduction  
At present, there are many various sources of air pollution at ports. The main components of such pollution include particulate matter (PM), volatile organic compounds (VOCs), nitrogen oxide (NOX), hydrocarbon (HC), etc. [1]. Air pollution seriously endangers the health of both the port staff and the surrounding residents. More specifically, when VOC gases exceed a certain concentration, it will lead to a series of negative symptoms such as eye and respiratory tract irritation, skin inflammation, sore throat, and fatigue, which easily damages the central nervous system by introducing blood and brain obstacles. As a result, it is a formidable challenge to identify VOC gases. One of the most effective gas identification methods is the electronic nose. Compared with traditional single sensors, the e-nose is more sensitive due to its cross-sensitivity to the collected data. Electronic nose systems have been studied by many scholars. To improve the portability of electronic noses, a real-time mobile air quality monitoring system was proposed in [2]. As a result, the application of electronic nose technology to VOC gases is a feasible solution. However, at present, the sampling environment of the electronic nose is not optimal, and the recognition rate is often reduced due to noise. Since the data collected by the electronic nose is typically high-dimensional, it slows the entire system. Therefore, how to improve the electronic nose performance is of great significance. To date, many researchers have proposed various solutions for the electronic nose. From the perspective of the sensor itself, one of the ways to make improvements is to refine the process, structure, and material of the sensors. However, the cost of this approach is relatively high. Alternatively, the issue can be approached by signal processing, which has attracted many researchers. For example, many scholars have attempted to improve the performance of electronic noses by improving their pattern recognition and feature extraction algorithms [3; 4; 5]. At present, many pattern recognition methods are developed. The pattern recognition methods typically used in electronic nose research include support vector machines, BP artificial neural networks, principal component analysis (PCA), linear discriminant analysis (LDA), k-nearest neighbor methods, decision tree, etc. [6; 7; 8; 9; 10; 11]. To address the problem of data collinearity when analyzing mixed gases, [PERSON] et al. combined multiple linear regression with PCA, subsequently proposing the partial least squares regression method to apply to electronic noses [12]. Meanwhile, to improve the recognition time in gas sensor arrays, a novel feature extraction method has been proposed, which could extract robust information with a short recognition time and response-recovery time [13]. Elsewhere, to quickly analyze the VOC pollution level on a farm, electronic nose technology was used to detect and analyze the gas in the pig house and the chicken house, respectively, through support vector machine (SVM) analysis [14]. The experimental results show that the support vector machine is an effective method in the identification of volatile gases on livestock and poultry farms. Moreover, the feasibility of detecting spray-dried porcine plasma (SDPP) using an electronic nose and near-infrared spectroscopy (NIRS) was explored and validated through PCA, which could offer an effective means to identify the use of SDPP in feed mixtures [15]. Sparse representation classification (SRC) has been shown to have high accuracy and certain robustness, even in the presence of noise or when handling incomplete measurements. Thus far, many classification problems have been successfully solved through SRC algorithms (such as face recognition [16; 17], image classification [18; 19], object tracking [20; 21], and medical detection [22]). At the same time, the SRC classification algorithm has been applied to electronic noses. For example, [PERSON] et al.