Chandraratne, MRSamarasinghe, SandhyaKulasiri, DFrampton, CMBekhit, AEDBickerstaffe, R2008-01-092003-081174-6696https://hdl.handle.net/10182/242Meat 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.pp.1-8enlambmeat industrymeat qualitymeat tendernessartificial neural networksmeat texturecomputer visionimage analysisOtherMarsden::280200 Artificial Intelligence and Signal and Image ProcessingMarsden::290100 Industrial Biotechnology and Food SciencesANZSRC::1402 Applied EconomicsANZSRC::10 TechnologyANZSRC::0899 Other Information and Computing Sciences