Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef

A total of 182 beef samples were minced and divided into calibration set (n=140) and independent validation set (n=42). Calibration models of NIRS (1000–1800nm) were built using partial least squares regression (PLSR) on the calibration set of samples. Both the coefficient of determination in calibr...

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Bibliographic Details
Published in:Meat science 2014-10, Vol.98 (2), p.110-114
Main Authors: Su, Huawei, Sha, Kun, Zhang, Li, Zhang, Qian, Xu, Yuling, Zhang, Rong, Li, Haipeng, Sun, Baozhong
Format: Article
Language:English
Subjects:
Animals
Beef
Calibration
Cattle
Chemical composition
Dietary Fats - analysis
Dietary Proteins - analysis
Least-Squares Analysis
Meat - analysis
Models, Theoretical
NIRS
Spectroscopy, Near-Infrared - methods
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Summary:A total of 182 beef samples were minced and divided into calibration set (n=140) and independent validation set (n=42). Calibration models of NIRS (1000–1800nm) were built using partial least squares regression (PLSR) on the calibration set of samples. Both the coefficient of determination in calibration (R2C) and the coefficient of determination in prediction (R2P) were over 0.98 for all chemical compositions. The ratio performance deviation (RPD) was 17.37, 5.12 and 10.43 for fat, protein and moisture, respectively. The results of the present study indicate the outstanding ability of NIRS to predict chemical composition in beef.
ISSN:0309-1740
1873-4138
DOI:10.1016/j.meatsci.2013.12.019