{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:52:39Z","timestamp":1760241159018,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T00:00:00Z","timestamp":1575936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJMI17DD"],"award-info":[{"award-number":["JPMJMI17DD"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper reports on-line mixture quantification with FAIMS. Ternary gas mixtures composed of acetone, ethanol, and diethyl ether were used for quantification. We succeeded in an on-line quantification of ppm-level concentration and even sub-ppm-level gases using the gradient descent method. It took 10 minutes to quantify the ternary mixture. However, it was too long, because we aim to track the temporal change of each component concentration in the mixture. Then, an algorithm based on feedback control was introduced to reduce the quantification time. The feedback method successfully tracked concentrations in three cases. The simulation result shows that the proposed method can reduce the quantification time.<\/jats:p>","DOI":"10.3390\/s19245442","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T10:52:41Z","timestamp":1575975161000},"page":"5442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS"],"prefix":"10.3390","volume":"19","author":[{"given":"Yasufumi","family":"Yokoshiki","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama-shi, Kanagawa 226-8503, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takamichi","family":"Nakamoto","sequence":"additional","affiliation":[{"name":"Laboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama-shi, Kanagawa 226-8503, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.jchromb.2018.06.018","article-title":"Human odor and forensics: Towards Bayesian suspect identification using GC \u00d7 GC\u2013MS characterization of hand odor","volume":"1092","author":"Cuzuel","year":"2018","journal-title":"J. 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