{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T02:40:10Z","timestamp":1790908810538,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,1,25]],"date-time":"2013-01-25T00:00:00Z","timestamp":1359072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categories: the type of thin film that is used, the type of gas, and the approximate ppm-level of the gas. These models mainly offer the advantage that features are learned from data instead of being hand-designed. We compare our results to a feature-based approach using samples with various ppm level of ethanol and trimethylamine (TMA) that are good markers for meat spoilage. The result is that deep networks give a better and faster classification than the feature-based approach, and we thus conclude that the fine-tuning of our deep models are more efficient for this kind of multi-label classification task.<\/jats:p>","DOI":"10.3390\/s130201578","type":"journal-article","created":{"date-parts":[[2013,1,25]],"date-time":"2013-01-25T11:06:55Z","timestamp":1359112015000},"page":"1578-1592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7181","authenticated-orcid":false,"given":"Martin","family":"L\u00e4ngkvist","sequence":"first","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-701-82, \u00d6rebro, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Coradeschi","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-701-82, \u00d6rebro, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Loutfi","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-701-82, \u00d6rebro, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Rayappan","sequence":"additional","affiliation":[{"name":"Centre for Nanotechnology & Advanced Biomaterials (CeNTAB) & School of Electrical & Electronics Engineering, SASTRA University, Thanjavur 613 401, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1142\/S0219581X1100943X","article-title":"Ethanol and trimethyl amine sensing by Zno-based nanostructured thin films","volume":"10","author":"Sivalingam","year":"2011","journal-title":"Int. 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