Fast classification of meat spoilage markers using nanostructured ZnO thin films and unsupervised feature learning.
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.
Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden. martin.langkvist@oru.se
Martin
M
Längkvist
Silvia
S
Coradeschi
Amy
A
Loutfi
John Bosco Balaguru
JB
Rayappan
D000465:Algorithms; D005504:Food Analysis / Q000379:methods*; D008460:Meat / Q000032:analysis*; D049329:Nanostructures / Q000737:chemistry*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine; D015034:Zinc Oxide / Q000737:chemistry*
D015034:Zinc Oxide