构建整粒油菜籽脂肪酸成分近红外反射光谱分析模型的研究

Study on Developing Calibration Models of Fat Acid Composition in Intact Rapeseed by Near Infrared Reflectance Spectroscopy

选用具有多年份、多地点、变异大的497份油菜籽育种材料组成原始样品集，光谱经散射和数学预处理利用改良偏最小二乘法（MPLS）构建各脂肪酸近红外反射光谱（NIRS）校正模型，同时采用二种不同用量的样品杯进行NIRS建模分析。结果表明，以8g样品校正建模效果最好，六种脂肪酸的校正决定系数为0．74～0．98。同时以3和0．6g样品分别发展的校正模型效果也较好，两者分析效果相近。各项决定系数（RSQ1，1-VR）高，相应的各项误差（SEC，SECV）较低。该研究以完整油菜籽为样品所建立的脂肪酸NIRS模型，可直接用于育种材料选择、突变体筛选和种质资源的评价等研究。

Four hundred ninety seven rapeseed samples, which feature multi-year, multi-loci and highly variant characteristics, were collected as a raw set. The NIR spectra were pretreated by scatter correction and mathematics treatments, and calibration models of fat acid composition of intact rapeseed were developed by using the algorithm method of modified partial least square （MPLS）. Meanwhile, three types of sample cups with different capacity were used to screen the suitable calibration model for rapeseed quality breeding. The results showed that the calibration model of 8-gram-sample was the best, and the calibration determination coefficient was in the range o[ 0.74-0. 98. The calibration effects of 3-gram-sample, which were similar to those of 0.6-gram-sample, were good with high determination coefficient （RSQ1, 1-VR） and low error （SEC, SECV）. Therefore, the calibration set with multl-year and multi-loci samples can improve the accuracy and repeatability of NIRS models. The fat acids NIRS models of intact rapeseed developed could be introduced into breeding lines＇ selection, mutant screening and germplasm evaluation.