{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:46:54Z","timestamp":1774525614080,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal\/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor Array Drift Dataset as a benchmark reported promising drift compensation results but often lacked robust statistical validation and may overcompensate for drift by suppressing class-discriminative variance. To address these limitations and rigorously evaluate improvements in sensor-drift compensation, we designed two domain adaptation tasks based on the UCI electronic-nose dataset: (1) using the first batch to predict remaining batches, simulating a controlled laboratory setting, and (2) using Batches 1 through n\u22121 to predict Batch n, simulating continuous training data updates for online training. Then, we systematically tested three methods\u2014our semi-supervised knowledge distillation method (KD) for sensor-drift compensation; a previously benchmarked method, Domain-Regularized Component Analysis (DRCA); and a hybrid method, KD\u2013DRCA\u2014across 30 random test-set partitions on the UCI dataset. We showed that semi-supervised KD consistently outperformed both DRCA and KD\u2013DRCA, achieving up to 18% and 15% relative improvements in accuracy and F1-score, respectively, over the baseline, proving KD\u2019s superior effectiveness in electronic-nose drift compensation. This work provides a rigorous statistical validation of KD for electronic-nose drift compensation under long-term temporal drift, with repeated randomized evaluation and significance testing, and demonstrates consistent improvements over DRCA on the UCI drift benchmark.<\/jats:p>","DOI":"10.3390\/informatics13010015","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:24:34Z","timestamp":1768901074000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sensor-Drift Compensation in Electronic-Nose-Based Gas Recognition Using Knowledge Distillation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3969-9194","authenticated-orcid":false,"given":"Juntao","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, National University of Singapore, Singapore 119077, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7058-2603","authenticated-orcid":false,"given":"Xianghao","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Stanford University, Stanford, CA 94305, USA"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TIM.2008.917189","article-title":"Electronic nose for black tea classification and correlation of measurements with \u201cTea Taster\u201d marks","volume":"57","author":"Bhattacharyya","year":"2008","journal-title":"IEEE Trans. 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