Precursor Chemical Mixtures Analysis Using Joint VMD Adversarial Autoencoder and Multitask CNN Algorithm via Gas Sensor

Precursor chemical mixture gases (PMGs) analysis plays a significant role in combatting drug-related crimes. However, existing intelligent methods for analyzing PMG components are constrained by weak signals, as well as signal coupling issues related to semiconductor gas sensors. To address this issue, a variational mode decomposition (VMD) adversarial autoencoder joint multitask convolutional neural network (VAE-MTCNN) algorithm is proposed. The proposed algorithm uses a semiconductor gas sensor MQ4, to collect signals in square wave temperature modulation, which are then processed by VMD to minimize signal coupling. The sub-signals are then augmented using the adversarial autoencoder through adversarial training. The construction of RGB digital portraits (RDPs) is used to enhance the validity of characteristic expressions by converting sub-signals to 2-D images. Finally, the MTCNN is designed to achieve PMG component recognition and quantification using two parallel task channels. The performance of the proposed method is validated using PMG samples, including acetone, ether and acetone and ether. Experimental results show that the VAE-MTCNN approach outperforms current state-of-the-art methods, with a PMG component recognition precision of 96.3% and a concentration quantification root mean square error (RMSE) of 3.50 ppm. Therefore, this method has great potential to improve drug-related crime detection.