Su Huawei, Sha Kun, Zhang Li, Zhang Qian, Xu Yuling, Zhang Rong, Li Haipeng, Sun Baozhong
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; College of Food and Wine, Yantai Research Institute of China Agricultural University, Yantai 264670, China.
Meat Sci. 2014 Oct;98(2):110-4. doi: 10.1016/j.meatsci.2013.12.019. Epub 2014 May 27.
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 (R(2)C) and the coefficient of determination in prediction (R(2)P) 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.
总共182个牛肉样本被切碎并分为校准集（n = 140）和独立验证集（n = 42）。使用偏最小二乘回归（PLSR）在校准样本集上建立了近红外光谱（1000 - 1800nm）的校准模型。所有化学成分在校准中的决定系数（R(2)C）和预测中的决定系数（R(2)P）均超过0.98。脂肪、蛋白质和水分的比率性能偏差（RPD）分别为17.37、5.12和10.43。本研究结果表明近红外光谱在预测牛肉化学成分方面具有出色的能力。
