文献类型： 外文期刊

作者： Li, JX ¹ ; Min, SG ² ; Zhang, HL ² ; Yan, YL ³ ; Luo, CB; Li, ZC;

作者机构： 1.China Agr Univ, Beijing Key Lab Crop Genet Improvement, Key Lab Crop Genom & Genet Improvement, Beijing 100094, Peoples R China

2.China Agr Univ, Beijing Key Lab Crop Genet Improvement, Key Lab Crop Genom & Genet Improvement, Beijing 100094, Peoples R China; China Agr Univ, Coll Sci, Beijing 100094, Peoples R China; China Agr Univ, Coll Informat, Beijing 100094, Peoples R China; Henan Acad Agr Sci, Crop Genet Res Inst, Zhengzhou 450002, Peoples R China

3.China Agr Univ, Beijing Key Lab Crop Genet Improvement, Key Lab Crop Genom & Genet Improvement, Beijing 100094, Peoples R China; China Agr Univ, Coll Sci, Beijing 100094, Peoples R China; China Agr Univ, Coll Informat, Beijing 100094, Peop

关键词： brown rice;partial least-squares regression (PLS);protein content;optimization object function;model optimization

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2006 年 26 卷 5 期

页码：

收录情况： SCI

摘要： A hundred and ninety one representative brown rice samples from the Chinese Rice Genebank and the DH population derived from the cross of japonica upland rice IRAT109 with paddy rice Yuefu were selected for this study. Their protein content range was 5.90%-14.50%. Near-infrared diffusive spectroscopy (NIDRS) and partial least square (PLS) were used to determine protein content with different wavelength ranges and data preprocessing methods for regression and information extraction. The object function [R/(1+RMSECV)] of quantitative model was defined, and the samples of calibration and validation tests were classified by projective distribution of PLS loadings. These methods were applied to the optimization of the calibration model. It is demonstrated that the calibration model developed by the spectral data pretreatment of the first derivative + standard vector normalization with the same spectral region (5000-9000 cm(-1)) resulted in the best determination of protein content in brown rice when the maximum values of the object function were reached. The maximum values of the object functions of calibration and validation sets were 0.701 and 0.687, respectively. Projective distributions of PLS loadings were used to validate the models, and the result was the same as that of validating model by object function [R/(1+RMSECV)].