A Four-Task Convolutional Neural Network Model for Real-Time Volatile Organic Compounds Detection

The electronic nose (E-nose) is an advanced technique that has attracted substantial attention across various domains, such as environmental monitoring and disease prevention. This study proposes an innovative four-task convolutional neural network (FT-CNN) model to enhance the performance of E-nose. In contrast to conventional E-nose systems, the proposed FT-CNN model not only accomplishes four tasks simultaneously—gas classification, concentration prediction, state assessment, and anomaly identification—but also facilitates real-time detection, thereby enabling immediate decision-making in dynamic environments. The model employs a unique two-block knowledge-sharing structure, significantly improving system efficiency. Additionally, noise injection and data segmentation mechanisms are incorporated to bolster the robustness and generalization of the model. The million-volume dataset, collected from a self-developed gas sensing system, underscores the model’s capacity to handle real-world variations and anomalies effectively. Experimental results demonstrate that the FT-CNN model achieves remarkable performance, with a gas classification accuracy of 97%, a sensor state assessment accuracy of 98%, an ${R}^{{2}}$ score exceeding 0.97 for concentration prediction, and an area under the curve (AUC) of 0.99 for anomaly identification. This comprehensive framework, integrating efficient data processing, noise immunity mechanisms, and real-time detection capabilities, demonstrates the remarkable potential of FT-CNN in advancing E-nose technology.