Predicting beef tenderness using color and multispectral image texture features |
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Authors: | Sun X Chen K J Maddock-Carlin K R Anderson V L Lepper A N Schwartz C A Keller W L Ilse B R Magolski J D Berg E P |
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Affiliation: | Department of Animal Science, North Dakota State University, Fargo, ND, USA; Department of Engineering, Nanjing Agriculture University, Nanjing, Jiangsu, People's Republic of China. |
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Abstract: | The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner-Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender. |
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Keywords: | Beef Tenderness SVM Color Multispectral image Stepwise |
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