Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning
| Autor: | Martin Längkvist, Silvia Coradeschi, Amy Loutfi, John Bosco Balaguru Rayappan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
electronic nose
Meat Support Vector Machine Computer science Food spoilage Boltzmann machine fast multi-label classification computer.software_genre lcsh:Chemical technology Biochemistry Article Analytical Chemistry Meat spoilage lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Principal Component Analysis business.industry Computer Sciences Reproducibility of Results Pattern recognition Atomic and Molecular Physics and Optics Nanostructures Support vector machine Datavetenskap (datalogi) Feature (computer vision) Principal component analysis Data mining Artificial intelligence Zinc Oxide business computer Feature learning Algorithms Food Analysis sensor material representational learning |
| Zdroj: | Sensors, Vol 13, Iss 2, Pp 1578-1592 (2013) Sensors (Basel, Switzerland) Sensors Volume 13 Issue 2 Pages 1578-1592 |
| Popis: | 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. Fuding agency: Department of Science & Technology, India |
| Databáze: | OpenAIRE |
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