应用可见-近红外光谱技术快速无损鉴别婴幼儿奶粉品种

为了快速无损鉴别婴幼儿奶粉品种,提出了结合偏最小二乘(PLS)法和人工神经网络(ANN)综合预测婴幼儿奶粉品种的新方法.获取婴幼儿奶粉样本在400～1 000 nm波段的漫反射光谱,采取平均平滑法和多元散射校正(MSC)进行预处理,用PLS建立校正模型进行模式特征分析及主成分的提取.经过交互验证法判别,提取7个主成分作为神经网络的输入变量,奶粉的品种值作为输出,建立了三层BP神经网络.9个典型品种的婴幼儿奶粉各取样本30个,共计270个作为训练集.随机抽取的各个品种的10个样本,共90个作为预测检验样本,结果表明,90个未知样本的品种预测准确率为100%.说明提出的方法具有很好的分类和鉴别作用,为婴幼儿奶粉的品种快速无损鉴别提供了一种新方法.

Fast Discrimination of Varieties of Infant Milk Powder Using Near Infrared Spectra

A new method for discrimination of varieties of infant milk powder by means of visible/near infrared spectroscopy (Vis/NIRS) (325-1 075 nm) was developed. Partial least square (PLS) was used to analyze the characteristics of the pattern. PLS compressed thousands of spectral data into a small quantity of principal components and described the body of spectra. The first seven principal components were confirmed as the best number of principal components. Then, these seven principal components were applied as the input to a back propagation neural network with one hidden layer. The infant milk powder varieties data were applied as the output of BP neural network. One hundred eighty samples containing nine typical varieties of infant milk powder were selected randomly, and they were used as a training set of the BP neural network model, and the remainder samples (total 90 samples) formed the prediction set. With a proper network training parameter, the recognition accuracy of 100% was achieved. This model is reliable and practicable. So the present paper could offer a new approach to the fast discrimination of varieties of infant milk powder.