| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 2449948 | Meat Science | 2014 | 5 Pages |
Abstract
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 (1000–1800 nm) 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.
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Food Science
Authors
Huawei Su, Kun Sha, Li Zhang, Qian Zhang, Yuling Xu, Rong Zhang, Haipeng Li, Baozhong Sun,