小麦蛋白质含量的性状遗传力较高, 通过选择蛋白质含量高的籽粒母本可以达到优质育种的预期效果。研究采用高光谱成像技术结合化学计量学方法建立多籽粒小麦粗蛋白平均模型来实现单籽粒小麦粗蛋白含量的快速预测。实验采集47份小麦样本(每份100粒)的高光谱图像并提取平均光谱信息, 通过联合区间偏最小二乘法筛选特征变量优化建立多籽粒小麦粗蛋白平均模型。模型的相关系数为0.94, 预测均方根误差为0.28%, 相对分析误差为3.30。通过图像处理提取出待测单籽粒小麦的高光谱图像, 应用平均模型预测单籽粒小麦在每个空间像素点的粗蛋白, 取其平均值作为该粒麦种的最终粗蛋白含量。经验证, 应用上述模型预测同一组样本的单籽粒小麦时, 不同籽粒的小麦粗蛋白含量确实存在差异, 但蛋白含量均围绕其所在样本的平均值浮动, 因此反映出采用平均模型预测单籽粒小麦蛋白的准确性和基本可行性。该方法的研究可以为小麦育种过程中高蛋白籽粒麦种的优选提供一种新思路, 推动小麦优质育种的发展。

The characteristics of wheat protein content has high heritability, so fine-quality breeding can be achieved by selecting the high-protein wheat seed. Combined with chemometric methods′ hyperspectral imaging technique was used to build the average model to achieve fast prediction of single wheat seed protein content. In the experiment, 47 unit wheat seed samples′ hyperspectral images were collected by GaiaChem-NIR system, and the average spectra was obtained by image process methods. Then, synergy interval partial least squares was applied to select the characteristic spectral regions to optimize the prediction model of wheat seed protein content. The optimal models′ determination coefficient is 0.94, the root mean square error of prediction is 0.28%, and the residual predictive deviation(RPD) is 3.30. Finally, the average model was applied to predict the protein content of each pixes of single wheat seed, and calculated the average as the single wheat grain protein content. The experimental results showed that different wheat grain′s protein content value predicted by the optimal model existed difference. Ueanwhile, the prediction values varied around the average protein content of its sample, which reflected that the average model is accurate and feasible to predict single wheat grain’s protein content. Therefore, the studied method provides a new way to select the high-protein wheat seed in the process of breeding, which can promote the development of wheat fine-quality breeding.