An Applied Study on Fourier Transform Near-Infrared Whole Spectroscopy Regression Analysis
ZHANG Lu-da1,WANG Tao1, YANG Li-ming1, ZHAO Li-li2, ZHAO Long-lian2, LI Jun-hui2, YAN Yan-lu2
1. College of Science, China Agricultural University, Beijing 100094, China 2. College of Information, China Agricultural University, Beijing 100094, China
Abstract: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.