花生籽仁中蔗糖含量近红外预测群体模型的建立及在突变体筛选中的应用

籽仁蔗糖含量是影响花生食味品质的重要因素。为了建立花生籽仁中蔗糖含量的高效检测技术,本研究采集了149份花生籽仁的近红外光谱,结合化学法测定籽仁蔗糖含量,采用偏最小二乘法 (PLS) 构建花生籽仁蔗糖含量近红外预测群体模型。结果显示,预测模型的决定系数(R²)为0.898,校正标准偏差(SEC)为0.253,20份外部验证材料的预测值和化学值的R²为0.873,预测模型具有较高的可信度,运用该模型筛选徐花17号诱变群体,从1 965份M₂单株籽仁中获得4份的突变体。本研究为优质食味花生种质资源的筛选和品种选育奠定了基础。

Establishment of Near Infrared Spectroscopy Model for Predicting Sucrose Content of Peanut Seed and Application in Mutants Selection

The sucrose content of seed kernel is an important factor affecting the edible quality of peanut(Arachis hypogaea L.).In order to establish an efficient technology for detecting sucrose content of peanut kernel,near infrared spectrum of a total of 149 peanut kernel samples were collected in this study,and the sucrose content of each sample was determined by chemical methods.A near-infrared calibration model of peanut seed sucrose content was established by partial least square(PLS)method.The results showed that the coefficient of determination(R²)was 0.898 and the squared error of calibration(SEC)was 0.253.The coefficient of determination between the predicted values and chemically tested values were 0.873 with 20 external samples,indicating that the model could be used to predict the sucrose content of peanut seeds very well.Using this model,four mutants with sucrose content higher than 6.00%were selected among 1965 EMS-induced M;individuals derived from Xuhua 17.This study laid the foundation for selecting germplasm resources and developing peanut varieties with good eating quality.