[Applied study on support vector machine (SVM) regression method in quantitative analysis with near-infrared spectroscopy].
This paper introduced the application of support vector machines(SVM) regression method based on statistics studytheory to the quantitative analysis with near-infrared (NIR) spectroscopy. Sixty-six wheat samples were used as experimental materials, and thirty-three of them were used as calibration samples. The protein contents and NIR spectra of the calibration samples were used to build SVM regression models by four different kernel functions. The protein content of the predicting samples are estimated by four different SVM regression models. All of the correlation coefficients between the estimated values by different SVM regression models and the standard chemical values of protein content by Kjeldahl's method are more than 0.97. The average absolute error is less than 0.32. To investigate the predicting effect, it is compared with PLS regression models. The result suggested that the SVM regression, which was built to estimate the protein content of wheat samples, can also be used in the quantitative analysis of real samples by NIR.
Zhang
Lu-da
LD
College of Science, China Argiculture University, Beijing 100094, China.
Jin
Ze-chen
ZC
Shen
Xiao-nan
XN
Zhao
Long-lian
LL
Li
Jun-hui
JH
Yan
Yan-lu
YL
D000465:Algorithms; D010940:Plant Proteins / Q000032:analysis / Q000592:standards; D012015:Reference Standards; D012044:Regression Analysis; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000592:standards*; D014908:Triticum / Q000378:metabolism
D010940:Plant Proteins