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Fast Classification...
Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning
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- Längkvist, Martin, 1983- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS
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- Coradeschi, Silvia, 1968- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS
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- Loutfi, Amy, 1978- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,AASS
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- Rayappan, John Bosco Balaguru (författare)
- SASTRA University, Thanjavur, India
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(creator_code:org_t)
- 2013-01-25
- 2013
- Engelska.
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 13:2, s. 1578-1592
- Relaterad länk:
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https://doi.org/10.3...
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https://oru.diva-por... (primary) (Raw object)
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- electronic nose
- sensor material
- representational learning
- fast multi-label classification
- Datavetenskap
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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