Department of Crop Sciences, Georg-August-Universität Göttingen, Von-Siebold-Strasse 8, 37075 Göttingen, Germany.
J Agric Food Chem. 2010 Jan 13;58(1):94-100. doi: 10.1021/jf9028199.
The development of oilseed rape cultivars with a high content of oleic acid (18:1) and a low content of linolenic acid (18:3) in the seed oil is an important breeding aim. Oil of this quality is increasingly being sought by the food and the oleochemical industry. Since the oil quality is determined by the genotype of the seed, a selection can be performed among single seeds of segregating populations. For this purpose a high-throughput Near-Infrared Reflectance Spectroscopy (NIRS) method using an automated sample presentation unit for single seeds of oilseed rape and a spectrometer equipped with a photodiode array detector was developed. Single-seed analyses have been accomplished with a throughput of up to 800 seeds per hour. Seeds from segregating populations of different origin were analyzed by NIRS and gas chromatography. Calibration equations were developed and validated applying the Modified Partial Least Square regression (MPLS) and LOCAL procedure. In three independent validations, standard errors of prediction corrected for bias between 2.7% and 3.7% for oleic acid and 1.2% and 1.8% for linolenic acid were determined using MPLS. Similar results were obtained applying the LOCAL procedure. The results show that the new high-throughput method can be applied to predict the oleic acid and linolenic acid content of single seeds of oilseed rape.
培育高油酸（18:1）和低亚油酸（18:3）含量的油菜品种是油菜育种的重要目标。这种品质的油越来越受到食品和油脂化工行业的青睐。由于油的质量取决于种子的基因型，因此可以在分离群体的单粒种子中进行选择。为此，开发了一种使用自动单粒种子进样装置和配备光电二极管阵列检测器的近红外反射光谱（NIRS）方法，用于油菜的高通量分析。单粒种子分析的通量可达每小时 800 粒。使用 NIRS 和气相色谱法分析了不同来源的分离群体的种子。应用修正的偏最小二乘回归（MPLS）和 LOCAL 程序开发和验证了校准方程。在三个独立验证中，应用 MPLS 确定油酸的预测校正偏差标准误差为 2.7%至 3.7%，亚油酸的预测校正偏差标准误差为 1.2%至 1.8%。应用 LOCAL 程序也得到了类似的结果。结果表明，新的高通量方法可用于预测油菜单粒种子的油酸和亚油酸含量。
Zotero 插件