Meat Sci. 2015 Feb;100:301-5. doi: 10.1016/j.meatsci.2014.10.028.
For Indonesian community, meatball is one of the favorite meat food products. In order to gain economical benefits, the substitution of beef meat with rat meat can happen due to the different prices between rat meat and beef. In this present research, the feasibility of FTIR spectroscopy in combination with multivariate calibration of partial least square (PLS) was used for the quantitative analysis of rat meat in the binary mixture of beef in meatball formulation. Meanwhile, the chemometrics of principal component analysis (PCA) was used for the classification between rat meat and beef meatballs. Some frequency regions in mid infrared region were optimized, and finally, the frequency region of 750-1000 cm(-1) was selected during PLS and PCA modeling.For quantitative analysis, the relationship between actual values (x-axis) and FTIR predicted values (y-axis) of rat meat is described by the equation of y= 0.9417x+ 2.8410 with coefficient of determination (R2) of 0.993, and root mean square error of calibration (RMSEC) of 1.79%. Furthermore, PCA was successfully used for the classification of rat meat meatball and beef meatball.
对于印度尼西亚社区来说，肉丸是他们最喜欢的肉类食品之一。为了获得经济利益，由于老鼠肉和牛肉之间的价格差异，可能会用老鼠肉代替牛肉。在本研究中，傅里叶变换红外光谱（FTIR）结合偏最小二乘法（PLS）多元校正用于定量分析肉丸配方中牛肉和老鼠肉的二元混合物中的老鼠肉。同时，主成分分析（PCA）的化学计量学用于区分老鼠肉丸和牛肉肉丸。优化了中红外区域的一些频率区域，最后，在 PLS 和 PCA 建模过程中选择了 750-1000 cm(-1) 的频率区域。对于定量分析，老鼠肉的实际值（x 轴）和 FTIR 预测值（y 轴）之间的关系由方程 y= 0.9417x+ 2.8410 描述，决定系数（R2）为 0.993，校准均方根误差（RMSEC）为 1.79%。此外，PCA 成功地用于区分老鼠肉肉丸和牛肉肉丸。
