Establishment of math models of NIRS analysis for protein contents in seed and it′s application in rice breeding

Chino. 研究利用近红外光谱分析法定量分析水稻完整籽粒粗蛋白含量的可行性，初步探讨水稻杂种后代蛋白质含量的分离和变异，以期为水稻的营养品质育种提供参考依据，提高育种效率。收集蛋白质含量变幅（5.90%～14.50%）较大的191份代表性水稻样品,采用偏最小二乘（PLS）法建立糙米粗蛋白预测的校正模型。通过比较光谱预处理方法在不同谱区的处理效果：采用一阶导数＋矢量标准化预处理、谱区为 11 998.9cm. 1～5 449.8 cm. 1和4 601.3 cm. 1～4 246.5 cm. 1建立校正模型的检验和预测效果最佳，糙米蛋白质的近红外测定值和化学测定值之间有较高的相关性和较低的误差；其决定系数为0.9886，相对标准偏差RSD为0.021，各项误差均在0.4以下。此外，利用该模型快速无破损的测定了水稻蛋白质育种的20个杂交组合的205个F2代单株，203个单株的马氏距离值在0.3以下，达到了试验精度的要求，分析结果表明：F2代群体单株间粗蛋白含量表现出广泛的变异，大部分单株蛋白质含量介于双亲之间，出现了超高亲和超低亲的单株，最高蛋白质含量达到15.3%。利用建立的各类误差较小近红外定量分析模型，实现了对育种亲本和中间育种材料的筛选鉴定；说明了通过蛋白质含量高的稻种资源与农艺性状优良的水稻品种杂交，低世代借助近红外分析技术辅助测定蛋白质含量可能是水稻高蛋白质育种的一条有效途径。

Inglés. To analyze the genetic variability of protein content in rice hybrid progenies, the feasibility of building calibration model to estimate crude protein content in intact rice kernel by near-infrared reflectance spectroscopy (NIRS) was studied. It is beneficial to provide the parameter and improve nutritional quality in grain breeding program. Using 191 rice samples with a broad range of crude protein content, 5.90%C14.50%, as materials, the chemometrical method of partial least square regression was used to establish the calibration model. The optimal model was developed by the spectral data pretreatment of the first derivative + vector normalization in 11 998.9 cm. 1C5 449.8 cm. 1 and 4 601.3 cm. 1C4 246.5 cm. 1, by analyzing different spectral data pretreatment and light frequency ranges. This model’s determination coefficient and relative standard deviation (RSD) were 0.9886 and 0.021, respectively. The model showed significant correlation and lower error between near-infrared value and true value, and had lower root of mean square errors than 0.4. The crude protein contents of another 205 seed samples (nondestructive brown rice) from the F2 populations of 20 crosses were estimated by the calibration model. The Mahalanobis distance of 203 individuals in F2 populations predicted by the model were lower than 0.3, and showed higher calibration precision. The results showed those populations had wide variation among individuals in crude protein content. In general, the great majority was in the middle of both counterpart parent, but some of them were found higher or lower than their parents in crude protein content, and the highest value reached 15.3%. Good calibration equation was successfully developed for protein content, the equation show satisfactory determination coefficients. The samples were scanned by NIRS and analyzed the protein content in parents and hybrid progenies. Finally, a probably effective way to improve protein content of rice was proposed: Firstly，the breeder makes some crosses between promising rice varieties with high yield and good quality and rice resource germplasm with high protein content, and secondly, the NIRS is used to evaluate protein content of the nondestructive brown rice in their early progeny population. This NIRS-assisted-selection could be a very efficient method to improve protein content in rice breeding programs.