棉花是一种重要的油料作物。 建立快速、 无损检测棉花种子含油量的方法, 对于棉花油分育种工作中的材料鉴定、 筛选具有重要意义。 利用近红外光谱仪采集118份不同油分含量棉花种籽的近红外漫反射光谱, 结合化学方法测定验证, 建立了棉籽油分含量快速无损检测的近红外模型。 光谱预处理方法采用一阶导数＋多元散射校正(MSC), 光谱范围5 446～8 848 cm-1, 主成分维数为5, 以基本覆盖陆地棉棉籽含油量范围的106份试验材料为校正样品集, 利用偏最小二乘法(PLS)建立了棉籽仁油分含量近红外反射光谱(NIR)校正模型。 校正模型决定系数R2=0.975, 校正标准差SEC=0.67。 用外部验证样品集进行外部验证, 对所建模型的实际预测能力进行检验。 结果表明, 油分含量预测值与化学值相关系数r=0.978, 预测结果误差范围0.1%～1.7%, 建立的模型具有很好的预测性。 利用建立的模型对784份育种材料进行了油分含量预测, 结果显示, 该模型应用可以加快棉花育种材料的油分鉴定。

Cotton is one of the important oil crops, and it is great significance for screening and identification of breeding materials to establish a method of the rapid, nondestructive testing of cotton seed oil content. In this study, near-infrared diffuse reflection spectroscopy of 118 high and low oil materials were adopted to establish models for fast nondestructive determining oil content of cottonseed using near infrared spectroscopy (NIR). One hundred and six cottonseed samples as calibration set that covered the range of seed oil content for upland cotton were used in this experiment. The spectral data of cottonseed were processed using the first derivative and multiplicative scatter correction (MSC). The correction NIR model of oil content was built based on partial least squares (PLS) method with the spectral regions 5 446～8 848 cm-1 and main components (5). The determination coefficient (R2) of calibration model was 0.975, standard error of calibration (SEC) was 0.67. The authors test the model’s actual ability to predict using external validation set. The correlation coefficient (r) of predicted values and the chemistry value was 0.978, the range of prediction error was 0.1%～1.7%. The model established has good predictability. The oil content of 784 breeding stocks were predicted by NIR model, statistical analysis of predictable results elucidated that the NIR model of oil content developed can be well applied to selective breeding and oil related study in cotton.