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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Detalles Bibliográficos
Autores principales: Längkvist, Martin, Coradeschi, Silvia, Loutfi, Amy, Rayappan, John Bosco Balaguru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649374/
https://www.ncbi.nlm.nih.gov/pubmed/23353140
http://dx.doi.org/10.3390/sl30201578

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649374/
https://www.ncbi.nlm.nih.gov/pubmed/23353140
http://dx.doi.org/10.3390/sl30201578

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