向日葵籽仁脂肪和脂肪酸含量近红外光谱模型的建立

为实现向日葵品质的快速无损检测,选取50份具有代表性的油用向日葵材料,采用偏最小二乘法（PLS）构建籽仁脂肪、亚油酸、油酸、硬脂酸和棕榈酸含量的近红外光谱（NIRS）模型。结果表明,脂肪、亚油酸、油酸含量模型校正和验证相关系数均大于0.96,且预测值与化学值相对误差均在10%以下,能够达到样品成分含量的快速测定。硬脂酸和棕榈酸含量模型校正相关系数分别为0.92和0.82,验证相关系数分别为0.83和0.74,预测值与化学值相对误差在4.66%~17.99%之间,可用于样品成分含量的初步预测。本研究构建的NIRS模型,有助于油用向日葵种质资源品质鉴定和快速筛选。

Establishment of Near-Infrared Spectroscopy Model for the Contents of Fat and Fatty Acids in Sunflower Husked Seeds

For achieving rapid and non-destructive testing of sunflower quality, 50 representative oil sunflower materials were being selected. Partial least squares (PLS) was used to construct near-infrared spectroscopy (NIRS) model of the contents of fat, linoleic acid, oleic acid, stearic acid, and palmitic acid in husked seeds. The results showed that the correlation coefficients of calibration and validation of the models for fat, linoleic acid, and oleic acid contents were all greater than 0.96, and the relative errors between the predicted values and the chemical values were all less than 10%, which could meet the rapid determination of the samples composition content. The correlation coefficients of calibration and validation of the models for stearic acid and palmitic acid were 0.92 and 0.82, and the verified correlation coefficients were 0.83 and 0.74, respectively. And the relative errors between the predicted values and the chemical values was between 4.66%-17.99%, which could be used for the preliminary prediction of the sample's composition content. The NIRS model constructed in this study is helpful for the quality identification and rapid screening of oil sunflower germplasm resources in the future.