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Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either compress transient response information or introduce unnecessary computational cost. This work proposes SDRMixer, a lightweight sensor-specific framework for mixed-gas concentration quantification. SDRMixer uses a parameter-free sparse dynamic response encoding to organize the original sensor response, baseline-referenced excitation, and smoothed response kinetics into a physically meaningful dynamic response field. A compact temporal-feature mixer is then applied over fixed response-stage tokens for simultaneous multi-gas regression. To improve calibration coverage, a response-consistent augmentation strategy is used during model training. The proposed framework is evaluated on a previously reported mixed-gas sensor array dataset containing NO2, NH3, CH4, and CO2 mixtures. Both augmentation-enriched calibration domain benchmarking and original-measurement-based validation are conducted to assess prediction performance, computational efficiency, and stability on measured calibration samples. The results show that SDRMixer provides a good trade-off between accuracy and efficiency compared with generic deep learning architectures and compact gas-sensing baselines. These findings indicate that explicit dynamic response encoding combined with lightweight temporal-feature mixing is an effective modeling strategy for compact mixed-gas quantification within the investigated calibration domain.
Zhang et al. (Mon,) studied this question.