【摘要】 筛选有代表性的191份糙米样品为试材,其中42份来自国家稻种资源库、149份来自水旱稻杂交产生的DH系,蛋白质含量变幅5·90%~14·50%,采用偏最小二乘法(PLS)建立模型,并构造模型的评价参数———目标函数R/(1+RMSECV),同时借助校正集和验证集两个载荷向量得分二维空间投影图,对近红外定量模型进行评价和优化。结果表明:在5000~9000cm-1范围内,预处理方法为一阶导数,校正模型和外部检验的目标函数值分别为0·701和0·687;两载荷向量得分直观分布图显示样品的聚类结果与目标函数筛选结果一致,也进一步验证了目标函数是模型评价和优化的有效指标。更多还原

【Abstract】 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 (5 000-9 000 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).更多还原