EEMD and GUCNN-XGBoost Joint Recognition Algorithm for Detection of Precursor Chemicals Based on Semiconductor Gas Sensor

The precursor chemical (PC) plays a crucial role in the drug manufacture. In recent years, PC intelligent recognition technology based on the analysis of semiconductor gas sensor signals has become a research hotspot due to PC's volatility. However, in practice, the weak discrimination for commercial semiconductor gas sensor signals of PC gases is frequent, making it difficult to accurately recognize PC gases using the existing intelligent recognition methods. To tackle this problem, this article proposes a data-driven model based on ensemble empirical mode decomposition (EEMD) and gate recurrent unit (GRU) union convolutional neural network XGBoost (GUCNN-XGBoost) methods for PC gases recognition. First, the signals are collected by the commercial semiconductor gas sensor (MQ2) under the square wave temperature modulation, and then processed by the EEMD to obtain the intrinsic mode functions (IMF1-IMF7). Second, the GUCNN-XGBoost model is constructed to automatically extracts the binary morphological characteristics (BMC) and the time sequence characteristics (TSC) of the IMF1-IMF6; the XGBoost is utilized as the final classifier instead of common softmax to realize the high precision of 97.54%. Subsequently, we employ the IMF7-XGBoost regression model to achieve the accurate estimation of PC gases concentration, which can result in an average mean absolute error (MAE) of 4.98. Finally, a typical comparative experimental analysis validates the effectiveness of the proposed approach.