期刊
MEAT SCIENCE
卷 143, 期 -, 页码 18-23出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2018.03.020
关键词
Computer vision system; Intramuscular fat; Stepwise regression; Support vector machine
资金
- Natural Science Foundation for Young Scientists of Jiangsu Province of China [BK20140715]
The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future.
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