应用近红外光谱分析技术结合化学计量学方法, 建立了奶粉脂肪和蛋白质含量测定的化学计量学建模新方法.首先采用Kernard-Stone法对校正集样本和预测集样本进行分类, 然后利用小波变换滤波技术对样品的近红外光谱进行压缩去噪处理, 结合滤波后重构光谱信号建立脂肪和蛋白质的径向基神经网络回归模型, 并分别对径向基网络的扩散常数spread值及小波变换中的小波基与压缩尺度三个参数进行了详细的讨论.脂肪模型在小波基为db2及小波尺度为4、spread值为3.5时的预测模型精度最好；蛋白质模型在小波基为db8及小波尺度为4、spread值为6时, 预测模型精度最好.所建立的脂肪和蛋白质校正模型的预测集相关系数(Rp)分别为0.990和0.994, 预测均方根误差分别为0.007与0.004.预测结果表明, RBF网络结合小波变换进行建模预测, 模型简单、稳健且精度较好, 该方法适合奶粉脂肪和蛋白质含量的快速、无损测定.

A new chemometric method for determining the contents of fat and protein in milk powder was established by using near infrared(NIR) spectroscopy combined with chemometric methods. The calibration and prediction sets were partitioned by Kernard-Stone algorithm. Wavelet transform(WT) was used for de-noising and compressing signals. The radical basis function neural networks(RBFNN) model for the contents of fat and protein was built by combining with the reconstruction spectral signal. Three parameters, i.e., the spread value of RBF network, the wavelet functions, and decomposition levels were discussed in detail. The results show that the precision of the prediction model is the best when wavelet function, compression level and spread value are db2, 4, and 3.5 for fat. In the same way, the precision is the best when wavelet functin, compression level and spread value are db8, 4, and 6 for protein. Correlation coefficients(Rp) of prediction set for the correction model of fat and protein are 0990 and 0994, and root mean square error prediction(RMSEP) is 0.007 or 0.004, respectively. The results also show that the model is easy and robust, and the prediction accuracy is improved by using RBFNN combined with WT for building NIR models. This method is suitable for determining the content of fat and protein in milk powder rapidly and nondestructively.