Sun Qian, Wang Jia-Hua, Han Dong-Hai
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Jul;29(7):1818-21.
The present paper presents a new NIR analysis method with partial least square regression (PLS) and artificial neural network (ANN) to improve the prediction precision of the protein model for milk powder. First, an efficient method named region selecting by genetic algorithms (RS-GA) was used to select the calibration region, and then the GA-PLS model was made to predict the linear part of the protein content in milk powder. And then in the region selected by RS-GA method, principal component analysis (PCA) was calculated. The principal components were taken as the input of ANN model. The remnant values by subtracting the standard values and the GA-PLS validation values were regarded as the output of ANN. The ANN model was made to predict the nonlinear part of the protein content. The final result of the model was the addition of the two model's validation values, and the root mean squared error of prediction (RMSEP) was used to estimate the mixed model. A full region PLS model (Fr-PLS) was also made, and the RMSEP of the Fr-PLS, GA-PLS and GA-PLS+PC-ANN model was 0.511, 0.440 and 0.235, respectively. The results show that the prediction precision of the protein model for milk powder was largely improved when adding the nonlinear port in the NIR model, and this method can also be used for other complex material to improve the prediction precision.
本文提出了一种结合偏最小二乘回归（PLS）和人工神经网络（ANN）的近红外分析新方法，以提高奶粉蛋白质模型的预测精度。首先，采用一种名为遗传算法区域选择（RS-GA）的有效方法来选择校正区域，然后建立GA-PLS模型来预测奶粉中蛋白质含量的线性部分。接着，在通过RS-GA方法选择的区域内，计算主成分分析（PCA）。将主成分作为ANN模型的输入。用标准值减去GA-PLS验证值后的残差值作为ANN的输出。建立ANN模型来预测蛋白质含量的非线性部分。模型的最终结果是两个模型验证值之和，并用预测均方根误差（RMSEP）来评估混合模型。还建立了全区域PLS模型（Fr-PLS），Fr-PLS、GA-PLS和GA-PLS+PC-ANN模型的RMSEP分别为0.511、0.440和0.235。结果表明，在近红外模型中加入非线性部分后，奶粉蛋白质模型的预测精度有了很大提高，该方法也可用于其他复杂物质以提高预测精度。
