Next Article in Journal
Temperature Grid Sensor for the Measurement of Spatial Temperature Distributions at Object Surfaces
Next Article in Special Issue
Determination of Odor Release in Hydrocolloid Model Systems Containing Original or Carboxylated Cellulose at Different pH Values Using Static Headspace Gas Chromatographic (SHS-GC) Analysis
Previous Article in Journal
A New Method for Flow Rate Measurement in Millimeter-Scale Pipes
Previous Article in Special Issue
Odor Sampling: Techniques and Strategies for the Estimation of Odor Emission Rates from Different Source Types
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,* , 1
, 1
 and 2
Received: 31 October 2012; in revised form: 9 January 2013 / Accepted: 16 January 2013 / Published: 25 January 2013
View Full-Text   |   Download PDF [345 KB, uploaded 21 June 2014]   |   Browse Figures
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 electronic nose; sensor material; representational learning; fast multi-label classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


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.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert