近红外光谱法非破坏性测定黄豆籽粒中蛋白质、脂肪含量

Nondestructive Analysis of Protein and Fat in Whole-kernel Soybean by NIR

采用傅里叶近红外漫反射光谱非破坏性分析 ,能够提供完整籽粒黄豆样品成分的含量信息 ,结合偏最小二乘回归法 (PartialLeast Squares,PLS) ,以 39个不同品种的完整籽粒黄豆样品建立蛋白质和脂肪含量近红外定量分析模型 ,其中蛋白质、脂肪含量分析模型的测定系数R2 分别为 99 30 ,97 5 2 ,相对标准偏差RSD分别为 0 76 %和 1 3% ,检验集的化学值与模型预测值的相关系数r分别为 0 94 73,0 86 95。用所建模型对 2 6 4个不同品种的黄豆样品进行预测 ,并采用R error指标来估计分析结果的误差 ,其中蛋白质和脂肪模型预测的最小相对误差分别为 0 0 4 %和 2 4 6 % ,最大相对误差分别为 2 4 5 %和 4 2 5 %。

Near-infrared diffusion reflectance spectroscopy is a fast technique that can provide component information about intact soybean samples. We have combined this technique with partial least-squares (PLS) regression to perform a quantitative determination of protein and fat contents in soybean samples. In calibration set, the NIR model determination coefficient R 2 of protein and fat is 0.993 0 and 0.975 2 respectively, and the relative standard deviation (RSD) is 0.76% and 1.3% respectively. The correlation coefficient r of validation set is 0.947 3 and 0.869 5 respectively. This NIR model is used to predict the contents of protein and fat in 264 soybean samples, using R-error to assess the deviation of analysis results. The minimum RSD of prediction of protein and fat is 0.04% and 2.46% respectively, and the maximum RSD of prediction of protein and fat is 2.45% and 4.25% respectively. These results are of great importance in early screening of crop breeding.