One-Class Drift Compensation for an Electronic Nose - Xiuxiu Zhu; Tao Liu; Jianjun Chen; Jianhua Cao; Hongjin Wang (Chemosensors, 2021)
Drift compensation is an important issue in an electronic nose (E-nose) that hinders the development of E-nose’s model robustness and recognition stability. The model-based drift compensation is a typical and popular countermeasure solving the drift problem. However, traditional model-based drift compensation methods have faced “label dilemma” owing to high costs of obtaining kinds of prepared drift-calibration samples. In this study, we have proposed a calibration model for classification utilizing a single category of drift correction samples for more convenient and feasible operations. We constructed a multi-task learning model to achieve a calibrated classifier considering several demands. Accordingly, an associated solution process has been presented to gain a closed-form classifier representation. Moreover, two E-nose drift datasets have been introduced for method evaluation. From the experimental results, the proposed methodology reaches the highest recognition rate in most cases. On the other hand, the proposed methodology demonstrates excellent and steady performance in a wide range of adjustable parameters. Generally, the proposed method can conduct drift compensation with limited one-class calibration samples, accessing the top accuracy among all presented reference methods. It is a new choice for E-nose to counteract drift effect under cost-sensitive conditions.