Improving E-Nose Performance: A Novel ELM-Based Dual-Level Joint Domain Adaptation Method for Sensor Drift Data

The theory and technology of electronic nose (E-nose) systems have been vigorously developed, and these systems have achieved success in many practical applications, such as medical diagnosis, food quality inspection, and environmental detection. However, the drift problem of a sensor array affects the industrialization and commercialization of E-nose systems. In this article, a novel ELM-based dual-level joint domain adaptation method (JDAELM) is proposed to effectively suppress drift and address the distribution discrepancy issue. Specifically, the proposed method implements joint domain adaptation (DA) at the feature level and label level. For source domain data without drift, the information of the data could be preserved as much as possible. Considering the inconsistent distribution caused by drift, the marginal and conditional distribution discrepancies are reduced to a minimum at the feature level to achieve domain alignment. To reduce the impact of pseudolabels on the model, we align the label space to achieve DA at the label level. By maximizing the Hilbert–Schmidt independence criterion, the relationship between the feature projection space and label projection space is strengthened in this model. The joint learning model is effectively solved by an efficient alternative optimization strategy. The average accuracy of the proposed method is 88.30% and 87.21% under long-term drift and short-term drift, respectively, and 96.41% on the instrument variation dataset, which is superior to that of other comparison methods in terms of accuracy. This proves that the JDAELM can be well adapted to long-term and short-term drift scenarios, and can effectively compensate for instrument variation drift caused by inherent differences.