精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全，精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法，123个样品作为校正集，采用偏最小二乘法(PLS)，分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型，以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据，通过比较，以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好，其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证，决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420，平均绝对误差和相对误差分别为0.326和13.89％。结果表明，利用近红外分析技术可以检测精料补充料中肉骨粉的含量。

Prediction of meat and bone meal contents in concentrate supplement by near infrared reflectance spectroscopy

In order to ensure feed safety, it is necessary to detect the meat and bone meal contents in concentrate supplement. The method of near infrared reflectance spectroscopy (NIRS) for predicting meat and bone contents in concentrate supplement was explored. The NIRS calibration models were developed from a calibration set of 123 samples by partial least squares (PLS) regression. The initial spectrum was pretreated by scatter correction, Savisky-Golay smoothing, first derivative and second derivative, respectively, and the methods of multiple scatter correction, Savisky-Golay smoothing and the second derivative were chosen as the best pretreatment based on the biggest coefficient of determination in calibration(R²) and the smallest standard errors(RMSEC). The R² and RMSEC were 0.9751 and 0.437, respectively. Thirty-four samples were chosen as validation set, the coefficient of determination in validation(r²) and standard errors(RMSEP) were 0.9749 and 0.420, and the absolute errors and mean relative errors were 0.3259 and 13.89%, respectively. It is concluded that NIRS can be used as a method to detect meat and bone meal contents in concentrate supplement.