【摘要】 基于近红外光谱技术,将偏最小二乘法(Partial Least Squares,PLS)和单隐层的反向传播网络(Back-Propagation Network,BP)联用并测定了鲜乳中4种主成分含量。用PLS法将原始数据压缩为主成分,取前3个主成分的14个数据输入网络,以Kolmogorov定理为依据,经过实验确定中间层的神经元个数为29,初始训练迭代次数为1000,建立了脂肪、蛋白质、乳糖、牛乳总固体4种主成分含量的预测校正模型。PLS-BP模型对样品4个组分含量的预测决定系数(R2)分别为:0.961、0.974、0.951、0.997;本研究为近红外光谱技术在鲜乳多组分快速检测提供了新思路。更多还原

【Abstract】 Partial least squares(PLS) and back-propagation network(BP) prediction model for the determination of fat,protein,lactose,total solids in raw milk had been established with good veracity based on near infrared spectroscopic technique. 14 peak value data from 3 principal components straight ahead compressed from original data by PLS were taken as inputs of while 4 predictive targets as outputs.According to kolmogorov theorem and experiment,29 nerve cells were chosen as hidden nodes for its prediction with the lowest error.Its training iteration times were supposed as 1000.Predictive correlation coefficients by the model are 0.961,0.974,0.951,0.997.The result shows a new idea for multi-component quantitative analysis of raw milk.更多还原