傅里叶变换近红外全谱回归分析的应用研究

文章以66个小麦样品为实验材料，其中33个为建模集，剩余33个为预测集，利用广义逆矩阵直接确定傅里叶变换近红外全谱分析回归模型中的回归系数，建立了用于蛋白质定量分析的近红外全谱回归模型。用此模型对预测集中的样品进行预测，结果与凯氏定氮法测定结果间的相关系数为r=0.979 9，平均相对误差为1.76%，表明由广义逆矩阵方法所建近红外全谱定量分析回归模型有较好的分析结果。所建模型不仅可用于对样品的实际分析，而且可根据回归模型中各个系数了解各个波长点处的光谱信息对模型预测值的贡献，从而可理解并解释傅里叶变换近红外全谱回归模型的物理学与化学意义。

An Applied Study on Fourier Transform Near-Infrared Whole Spectroscopy Regression Analysis

In the present paper, 66 wheat samples were used as experimental materials, 33 of them were used for building the quantitative analysis model of protein content, and the rest composed the prediction set. Using Moore-Penrose matrix, we estimated directly the regression coefficients of the regression analysis model with Fourier transform near-infrared(FTNIR)whole spectroscopy. The samples of prediction set were analyzed, and the correlation coefficient is 0.979 9 between the prediction values of the near-infrared model and the standard chemical ones by Kjeldahl's method, and the average relative error is 1.76%. Using Moore-Penrose matrix, we can not only get the near-infrared spectroscopy analysis model's regression coefficients, but also know their contribution at every wavelength point. Consequently we can understand and explain the physical and chemical significance of the FTNIR whole spectroscopy regression model.