[Study on the application of ridge regression to near-infrared spectroscopy quantitative analysis and optimum wavelength selection].
In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl's values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model's predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl's values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.
Zhang
Man
M
College of Science, China Agricultural University, Beijing 100083, China. zhangman_ym@sina.com
Liu
Xu-Hua
XH
He
Xiong-Kui
XK
Zhang
Lu-Da
LD
Zhao
Long-Lian
LL
Li
Jun-Hui
JH
D016018:Least-Squares Analysis; D008962:Models, Theoretical; D010940:Plant Proteins / Q000737:chemistry*; D012044:Regression Analysis; D019265:Spectroscopy, Near-Infrared; D014908:Triticum / Q000737:chemistry*
D010940:Plant Proteins