Response-Patch CNN–Transformer Multitask Learning Model for VOCs Identification Using Electronic Nose

The electronic nose system is susceptible to cross-sensitivity, noise, and baseline drift, making simultaneous qualitative identification and quantitative prediction of volatile organic compounds (VOCs) challenging. To address these issues, this study proposes a CNN–Transformer multitask learning model based on response-patch sequences. A multichannel activity curve is first constructed to locate response onset and extract fixed-length samples. The long response sequence is then divided into ordered patches using a sliding window. A CNN captures local dynamic patterns associated with signal rise, peak, and recovery, while learnable positional embeddings and a Transformer model the sequential order and long-range dependencies among patches. Task-specific squeeze-and-excitation attention branches adaptively enhance classification- and regression-related features, and an uncertainty-weighted loss balances the two learning objectives. With a patch length of 75 and a stride of 37, the model achieved an Accuracy of 0.9717, an F1-score of 0.9696, an MAE of 5.0789 ppm, and an R2 of 0.9467. Comparative and ablation experiments confirmed its superior overall performance and the effectiveness of the proposed modules. The method provides a feasible solution for the integrated identification and quantitative analysis of VOCs in electronic-nose applications.