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

• Main Authors: John Bosco Balaguru Rayappan, Amy Loutfi, Martin Längkvist, Silvia Coradeschi
• Format: Article
• Language: English
• Published: MDPI AG 2013-01-01
• Series: Sensors
• Subjects: electronic nose; sensor material; representational learning; fast multi-label classification
• Online Access: http://www.mdpi.com/1424-8220/13/2/1578

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.