Rapid Classification and Concentration Prediction of VOC Gaseous Mixtures Using Sensor Array with Multi-Task Learning

Sensor array is the core of electronic nose (E-nose) system, while pattern recognition algorithm is an important component of the system. So, the sensor array optimization and the algorithm research cannot emphasize the importance too much. Volatile organic compounds (VOCs) mixture are ubiquitous in environmental monitoring, diseases screening, industrial emissions, and natural environments. Due to the flammability and potential health impacts, rapid and accurate detection of VOCs are crucial. Currently, E-nose systems for gas detection build separate models and random number of sensors for classification and concentration prediction, which lead to low efficiency and increased complexity. To address these issues, this study proposed a novel multi-task learning (MTL) framework combined with gated recurrent unit (GRU) and feature enhancement module (MTL-GRUA), performing both gas classification and concentration prediction tasks simultaneously. The model achieved over 99% accuracy in classification tasks for acetone, ethanol, and their mixture, and demonstrated high precision in concentration prediction tasks. By optimizing the hyperparameters of the network structure of MTL-GRUA by a particle swarm optimization (PSO) algorithm and using first 100s data instead of stable phase data, the detection efficiency was increased by 63%, achieving rapid detection. Furthermore, the experiment verified that even with an optimization in the number of sensors from 8 to 3, acceptable detection results could still be obtained. The application of multi-task learning proposed in this work provide a more efficient method for gas detection task.