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

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-do...

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Bibliographic Details
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

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