Prediction of lamb tenderness using texture features
dc.contributor.author | Chandraratne, Meegalla R. | |
dc.contributor.author | Samarasinghe, Sandhya | |
dc.contributor.author | Kulasiri, D. | |
dc.contributor.author | Frampton, Christopher M. | |
dc.contributor.author | Bekhit, A. E. D. | |
dc.contributor.author | Bickerstaffe, Roy | |
dc.date.accessioned | 2008-01-09T03:46:49Z | |
dc.date.issued | 2003-08 | |
dc.description.abstract | Meat quality is a subject of growing interest. The meat industry, in response to consumer demand for products of consistent quality, is placing more and more emphasis on quality assurance issues. Tenderness is an important quality parameter. An accurate, consistent, rapid and non-destructive method to evaluate meat tenderness is needed in the meat industry. Recent advances in the area of computer vision have created new ways to monitor quality in the food industry. This study determines the usefulness of raw meat surface characteristics in cooked meat tenderness prediction, and the use of neural network models to relate lamb tenderness with geometric and textural data extracted from lamb chop images. | en |
dc.format.extent | 1-8 | en |
dc.identifier.issn | 1174-6696 | en |
dc.identifier.uri | https://hdl.handle.net/10182/242 | |
dc.language.iso | en | |
dc.publisher | Lincoln University. Applied Computing, Mathematics and Statistics Group | |
dc.publisher.place | Lincoln, Canterbury | en |
dc.relation | The original publication is available from - Lincoln University. Applied Computing, Mathematics and Statistics Group - http://hdl.handle.net/10182/242 | en |
dc.relation.ispartofseries | Applied Computing Research Report | en |
dc.subject | lamb | en |
dc.subject | meat industry | en |
dc.subject | meat quality | en |
dc.subject | meat tenderness | en |
dc.subject | artificial neural networks | en |
dc.subject | meat texture | en |
dc.subject | computer vision | en |
dc.subject | image analysis | en |
dc.subject.anzsrc | ANZSRC::1402 Applied Economics | en |
dc.subject.anzsrc | ANZSRC::10 Technology | en |
dc.subject.anzsrc | ANZSRC::0899 Other Information and Computing Sciences | en |
dc.subject.marsden | Marsden::280200 Artificial Intelligence and Signal and Image Processing | |
dc.subject.marsden | Marsden::290100 Industrial Biotechnology and Food Sciences | |
dc.title | Prediction of lamb tenderness using texture features | en |
dc.type | Monograph | |
lu.contributor.unit | Lincoln University | |
lu.contributor.unit | Faculty of Agriculture and Life Sciences | |
lu.contributor.unit | Department of Wine, Food and Molecular Biosciences | |
lu.contributor.unit | Faculty of Environment, Society and Design | |
lu.contributor.unit | Department of Environmental Management | |
lu.identifier.orcid | 0000-0003-2943-4331 | |
pubs.publication-status | Published | en |
pubs.publisher-url | http://hdl.handle.net/10182/242 | en |
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