{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T05:06:53Z","timestamp":1789189613538,"version":"build-2803163510"},"reference-count":21,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101348"],"award-info":[{"award-number":["42101348"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["42101348"],"award-info":[{"award-number":["42101348"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2025NSFSC0328"],"award-info":[{"award-number":["2025NSFSC0328"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2025NSFSC0328"],"award-info":[{"award-number":["2025NSFSC0328"]}],"id":[{"id":"https:\/\/ror.org\/04323m874","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Single-sensor gas classification remains challenging because metal oxide semiconductor sensors exhibit limited selectivity and different gases can produce similar response patterns. This paper proposes a multi-scale temporal-dependency-learning framework that combines reservoir-based multi-scale dynamic learning (MsDL) with lagged-correlation features. To reduce information leakage caused by window construction, each continuous gas-response sequence is divided chronologically into training, validation, and test blocks before window extraction, and 2000 raw samples are excluded between adjacent subsets. The primary evaluation uses non-overlapping windows and fits all data-dependent preprocessing using the training subset only. On a dataset containing seven volatile organic compounds measured using a single sensor, the proposed MsDL+Corr representation with a random-forest classifier achieves a test accuracy of 92.14% and a macro-F1 score of 92.08%. Across ten random-forest seeds, the accuracy is 92.43% \u00b1 0.37%. The corresponding micro-average one-vs-rest AUC is 0.9948 (99.48%); this threshold-independent ranking metric is distinct from classification accuracy. Feature ablation shows that combining MsDL and correlation features improves the macro-F1 score over either branch alone. These results demonstrate that temporal-dependency representations provide useful discriminative information under a conservative temporally separated evaluation protocol.<\/jats:p>","DOI":"10.3390\/s26185763","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:54:19Z","timestamp":1789055659000},"page":"5763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale Temporal-Dependency Learning for Single-Sensor Gas Classification"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0187-4565","authenticated-orcid":false,"given":"Longlong","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-2407","authenticated-orcid":false,"given":"Gen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1430-1985","authenticated-orcid":false,"given":"Zhicheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Semiconductor System Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, R., Guo, R., Jiao, M., Wang, G., and Zhao, Z. 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