# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 1 Introduction  
Machine olfaction is widely used in gas classification and calibration of accurate concentration estimation. For example, in terms of food safety, it is used to detect the purity and quality of food [1; 2; 3]. In terms of environmental protection, it is used for a wide range of air quality monitoring [4; 5]. In medical cases, it is used to detect diseases [6]. The research on sensor drift compensation can effectively improve detection accuracy. In the face of sensor drift, in order to avoid tedious calibration tasks and save costs, many researchers have studied drift compensation algorithms for many years, proposing different solutions [7; 8; 9; 10; 11; 12]. The most important methods can be divided into three types: the first is the component correction method; the second is the adaptive method; the third is the machine learning method. For example, [PERSON] et al. proposed an orthogonal signal correction method (orthogonal signal correction, OSC). This method makes the corrected signal retain as much useful information as possible by removing the linearly irrelevant part of the domain target matrix in the original signal [13]. [PERSON] et al. used the OSC method to preprocess the data, and then optimized the RBF network through the particle swarm optimization algorithm. In order to detect wound infection, good results were obtained [14]. [PERSON] et al. proposed a component correction principal components analysis (CCPCA) algorithm based on the OSC algorithm [15]. This algorithm first assumes that the drift has a preferred direction in the measurement space rather than a random distribution and finds the direction of the drift through the method of principal component analysis (PCA). The drift direction is removed by the measurement matrix, and irrelevant information in the data is removed, thereby increasing the stability and increasing the generalization ability of the classification model. The adaptive method is a passive drift compensation method that matches the trained model to the current sensor output by modifying the parameters in the classification algorithm [16; 17]. There are two main methods: adaptive resonance theory (ART) and self-organizing feature mapping (SOM). [PERSON] et al. combined adaptive resonance theory with neural networks for gas identification. For overlapping clusters and non-overlapping clusters, different approaches to the drift problem have been proposed [18]. [PERSON] et al. proposed a novel mSom neural network approach to improve gas classification for multi-sensor systems. The algorithm adapts to changes in the data distribution brought about by the drift problem by repeating the self-training process by using multiple self-organizing graphs that approximate the statistical distribution of a single odor set [19]. Machine learning methods can also be used to address sensor drift [17; 20; 21]. This approach automatically adapts the classification to drift, thereby reducing the impact of drift problems, rather than explicitly describing or computing drift. [PERSON] et al. proposed an ensemble method based on support vector machines [22], a method of weighted summation of classifiers trained at different time points. In order to reduce the impact of cost and sensor drift, semi-supervised algorithms with both the advantages of supervised learning and unsupervised learning are introduced into the field of machine olfactory. Combining the domain adaptation algorithm with the domain semi-supervised algorithm, in [23], [PERSON] et al. proposed a method to construct a classifier using a weighted geodesic flow kernel (GFK) combined manifold regularization. Combined kernels are defined on multiple curves between source domain data and target domain data using unlabeled data. Domain adaptation has become a better method to solve sensor drift. Domain adaptation is a type of transfer learning that utilizes informative source domain samples to improve the performance of target domain models [12; 24; 25; 26].