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Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0002-3122-693X
SASTRA University, Thanjavur, India.
2013 (English)In: Sensors, ISSN 1424-8220, Vol. 13, no 2, 1578-1592 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 13, no 2, 1578-1592 p.
Keyword [en]
electronic nose, sensor material, representational learning, fast multi-label classification
National Category
Computer Science
Research subject
Computer Science
URN: urn:nbn:se:oru:diva-34598DOI: 10.3390/s130201578ISI: 000315403300012ScopusID: 2-s2.0-84873853951OAI: diva2:710523

Fuding agency: Department of Science & Technology, India 

Available from: 2014-04-07 Created: 2014-04-07 Last updated: 2015-02-25Bibliographically approved
In thesis
1. Modeling time-series with deep networks
Open this publication in new window or tab >>Modeling time-series with deep networks
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014. 56 p.
Örebro Studies in Technology, ISSN 1650-8580 ; 63
multivariate time-series, deep learning, representation learning, unsupervised
National Category
Computer and Information Science
Research subject
Information technology
urn:nbn:se:oru:diva-39415 (URN)978-91-7529-054-6 (ISBN)
Public defence
2015-02-02, Hörsalen, Musikhögskolan, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2015-12-28Bibliographically approved

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