花生籽仁含油量近红外模型的构建及其应用

Construction and application of near infrared ray model for oil content prediction in peanut kernel

花生(Arachis hypogaea L.)籽仁含油量是花生品质评价的重要指标,建立快速高效的含油量检测方法,对加快高油花生品种选育意义重大。本研究选用高油亲本宇花14(含油量59.32%)与低油亲本LOP215(含油量48.97%)杂交构建的RIL群体为建模材料,使用Thermo公司(美国)生产的AntarisⅡ型傅立叶变换近红外光谱分析仪对229份样品籽仁进行光谱采集,随后测定籽仁含油量。利用偏最小二乘法(partial least squares,PLS)构建花生籽仁含油量近红外定标模型,该模型的内部验证均方差(root mean square error of cross validation,RMSECV)为0.885,相关系数R^(2)=0.9147。选用未参与建模的21份花生材料对该模型进行外部验证,模型预测值和化学测定值的决定系数R^(2)=0.9492,表明该模型可适用于花生籽仁含油量检测。利用该模型对宇花14与LOP215杂交后代群体进行筛选,获得含油量超过55%的优良株系21个,含油量低于48%的株系9个,可为花生高低含油量品种选育提供种质材料。

Kernel oil content is an important index for peanut quality evaluation.It is of great significance to establish a rapid and efficient oil content detection method for accelerating the breeding of high oil peanut varieties.The RIL population constructed by crossing high oil parent Yuhua 14 with oil content of 59.32% and LOP215 with oil content of 48.97% was used as the modeling material,and the spectra of 229 samples were collected by using Antaris Ⅱ type fourier transform near infrared spectrometer produced by thermo company(USA),and then the oil content of seed kernel was determined.The partial least squares(PLS)method was used to construct the near infrared calibration model of peanut kernel oil content.The root mean square error of cross validation(RMSECV)of the model was 0.885,and the correlation coefficient R^(2)=0.9147.Twenty-one peanut materials not involved in the modeling were selected for external validation of the model,and the coefficient of determination of predicted value and chemical determination value of the model R^(2)=0.9492,indicating that the model can be applied to determination of oil content in peanut kernels.Twenty-one lines with oil content more than 55% and 9 lines with oil content less than 48%were obtained by screening from the progeny population of crossing between Yuhua 14 and LOP215,which can provide germplasm materials for breeding high or low oil content peanut varieties.