Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning
2013 (English)In: Sensors, ISSN 1424-8220, Vol. 13, no 2, 1578-1592 p.Article in journal (Refereed) Published
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.
electronic nose, sensor material, representational learning, fast multi-label classification
Research subject Computer Science
IdentifiersURN: urn:nbn:se:oru:diva-34598DOI: 10.3390/s130201578ISI: 000315403300012ScopusID: 2-s2.0-84873853951OAI: oai:DiVA.org:oru-34598DiVA: diva2:710523
Fuding agency: Department of Science & Technology, India 2014-04-072014-04-072015-02-25Bibliographically approved