特征根回归法近红外光谱定量分析研究

Study on Quantitative Analysis with Near-infrared Spectra Using Latent Root Regression Model

本文以大豆样品为实验材料 ,研究了特征根回归法近红外光谱定量分析。用 40个大豆样品的近红外光谱数据建立测定大豆蛋白质含量的特征根回归模型 ,预测另外 32个大豆样品的蛋白质含量 ,结果同PLS回归方法进行了比较 ,表明特征根回归模型可用于生物样品的近红外光谱定量分析。特征根回归法是对PCR建模方法改进的又一种化学计量学定量分析校正方法 ,该方法在对样品光谱提取主成份时考虑了待分析组分的作用 ,因此所建立的定量分析模型有好的分析效果。研究结果进一步表明 ,以样品近红外光谱建立定量分析模型 。

The Latent root regression model with near-infrared spectra of 40 soybean samples was founded for analyzing the content of soybean protein in this study. The contents of protein in another 32 soybean samples were predicted by this model. The predicting results were compared with PLS, which shows that the latent root regression model can practically be used for the quantitative analysis of the biological samples with near-infrared spectra. This method is a new kind of chemometrics calibration method, which is modified from PCR. Because the method takes the role of sample composition into account when extracting the principal component from the NIR spectra of samples, the model has a good result in analyzing samples. Further more, the results showed that it is necessary to take account of the role of sample composition when building quantitative analysis model using NIR spectra.