【摘要】 提出一种偏最小二乘法(PLS)和人工神经网络(ANN)结合用于近红外光谱(NIRS)的分析方法,以提高奶粉蛋白质模型的预测精度。首先采用基于遗传算法的波长选择法(RS-GA)优化光谱数据,建立GA-PLS模型预测奶粉蛋白线性部分;然后在RS-GA法选择的波段上进行主成分分析(PCA),以主成分的得分矩阵作为ANN模型输入层,以GA-PLS预测值与真实值之差作为输出层,建立PC-ANN模型预测其非线性部分。最终预测结果为两个模型预测值之和,以模型的预测标准偏差(RMSEP)作为评价指标,以便考察新方法的有效性。同时建立线性的全谱模型(Fr-PLS),其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:考虑奶粉蛋白含量近红外模型的非线性部分,可以显著提高模型的预测精度,该方法也可为其它复杂体系模型精度的提高提供借鉴。更多还原

【Abstract】 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.更多还原