# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 1 Introduction  
Non-destructive agricultural product testing technology is a novel testing approach that can accurately determine characteristics linked to the intrinsic quality of a test object without causing damage. This technology has been increasingly essential in the agricultural economy in recent years because of its high efficiency and non-destructive nature in determining the quality of agricultural products. In this arena, electronic nose technology, as an advanced gas analysis tool, offers a novel method for determining the quality of agricultural products by detecting the volatile organic compounds (VOCs) emitted by them. The e-nose system comprises two major components: a gas sensor module and a data processing module. Its operating principle is based on mimicking the olfactory mechanism of animals in nature, with gas sensors adsorbing and reacting to gas particles, resulting in potential changes. The signal processing module captures these potential changes and, following several complicated data processing steps, converts them into quantifiable gas concentration measurements. The application of electronic nose technology is not limited to agricultural products [1] but is also promising in other industries such as food [2; 3; 4]and pharmaceuticals [5; 6; 7]. The versatility and sensitivity of this technology make it an important tool in modern NDT technology. With the advancement of sensor technology and data processing algorithms, it is expected that electronic nose technology will play a greater role in the future of agricultural product quality inspection and other related industries. Specifically, in the field of agricultural products, electronic noses can detect some indicators through gas detection, such as the quality and maturity of agricultural products, etc., of which tea [8], juibube [9], and other crops because of their aroma are very important indicators, so gas detection is an often-used means. Our choice of tea as a representative, because of its different types, fermentation, and storage of different conditions such as the year, will lead to tea emissions of organic compounds having a gap, and the aroma substances mainly include alcohols (chlorite, phenylethanol, neurotoxin, etc. ), aldehydes (hexanal, 2-methylbutanal, etc. ), and ketones (\(\beta\)-damascene a, 10-3-1, etc.) [10]. Therefore, it is a convenient and effective way to obtain the external physical indexes without destroying its structure through the electronic nose. However, gas-sensitive sensors in electronic noses may exhibit response drift phenomena due to various variables, including sensor aging, olfactory intoxication, and environmental variations. These drift problems have an impact on the detection accuracy and operating stability of electronic noses in practical applications. [PERSON] and other researchers [11; 12] have conclusively demonstrated that ambient temperature and humidity are key factors in the drift of electronic noses. Nowadays, there are two main types of compensation: one is to improve the performance of the sensor by replacing the material [13], but this approach is more costly and less mature in terms of industrial production, so it is less often attempted; there is also compensation through mathematical modeling, which can be targeted to improve the efficiency of the sensor without changing the existing process. The other one is to compensate by mathematical modeling, which can be used to improve the efficiency of the sensor without changing the existing process, so more researchers try this aspect, and we also use the mathematical modeling method for compensation in this experiment. For example, [PERSON] [14] presented a compensation strategy for an electronic nose based on domain adaption, which tackles the distortion problem of electronic nose detection caused by drift using a neural network's entropy computation labeling ability. [PERSON] et al.