Sensors 2013, 13(2), 1578-1592; doi:10.3390/s130201578
Article

Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning

1 Center for Applied Autonomous Sensor Systems, Örebro University, SE-701-82, Örebro, Sweden 2 Centre for Nanotechnology & Advanced Biomaterials (CeNTAB) & School of Electrical & Electronics Engineering, SASTRA University, Thanjavur 613 401, Tamil Nadu, India
* Author to whom correspondence should be addressed.
Received: 31 October 2012; in revised form: 9 January 2013 / Accepted: 16 January 2013 / Published: 25 January 2013
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Abstract: 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.
Keywords: electronic nose; sensor material; representational learning; fast multi-label classification

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MDPI and ACS Style

Längkvist, M.; Coradeschi, S.; Loutfi, A.; Rayappan, J.B.B. Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. Sensors 2013, 13, 1578-1592.

AMA Style

Längkvist M, Coradeschi S, Loutfi A, Rayappan JBB. Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning. Sensors. 2013; 13(2):1578-1592.

Chicago/Turabian Style

Längkvist, Martin; Coradeschi, Silvia; Loutfi, Amy; Rayappan, John B.B. 2013. "Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning." Sensors 13, no. 2: 1578-1592.

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