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[利用近红外反射光谱法测定玉米籽粒中的脂肪酸浓度]

[Measuring fatty acid concentration in maize grain by near-infrared reflectance spectroscopy].

作者信息

Yang Xiao-Hong, Guo Yu-Qiu, Fu Yang, Hu Jie-Yun, Chai Yu-Chao, Zhang Yi-Rong, Li Jian-Sheng

机构信息

National Maize Improvement Center of China, Key Lab of Crop Genetics and Breeding of Beijing, China Agricultural University, Beijing 100094, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Jan;29(1):106-9.

Abstract

The fatty acid concentrations in maize grain were analyzed with a set of 294 samples including normal inbred lines, high-oil inbred lines and high-oil recombinant inbred lines (RIL). The method of partial least squares (PLS) regression with internal cross validation was employed to develop the measuring models of near-infrared reflectance spectroscopy (NIRS) for concentrations of four major fatty acids, palmitic, stearic, oleic and linoleic acids, as well as oil concentration in maize grain. The NIRS models were accurate for oleic acid, linoleic acid and oil concentrations. The determination coefficients of these models in cross validation were 0.89, 0.88 and 0.91, respectively; the determination coefficients in external validation were 0.86, 0.84 and 0.92, respectively; and the ratio of standard deviation (SD) to root mean square error of validation (RMSEV) in both calibration and external validation sets (RSC(P)) was higher than 2.5. But the models for palmitic and stearic acid concentrations were not accurate enough with determination coefficients in cross validation and external validation lower than 0.80, and RSC(P) lower than 2.5. Further practical validation showed that the predicted results by using NIRS models for oleic acid, linoleic acid and oil concentrations were accurate and reliable, which will be a useful approach to the measurement of a large number of breeding samples during genetic improvement of oil quality and quantity in maize.

摘要

对包括普通自交系、高油自交系和高油重组自交系(RIL)在内的294份玉米籽粒样本进行了脂肪酸浓度分析。采用具有内部交叉验证的偏最小二乘(PLS)回归方法,建立了近红外反射光谱(NIRS)对玉米籽粒中棕榈酸、硬脂酸、油酸和亚油酸四种主要脂肪酸浓度以及油浓度的测量模型。NIRS模型对油酸、亚油酸和油浓度的预测较为准确。这些模型在交叉验证中的决定系数分别为0.89、0.88和0.91;在外部验证中的决定系数分别为0.86、0.84和0.92;校准集和外部验证集的标准差(SD)与验证均方根误差(RMSEV)之比(RSC(P))均高于2.5。但棕榈酸和硬脂酸浓度模型的准确性不足,交叉验证和外部验证中的决定系数均低于0.80,RSC(P)低于2.5。进一步的实际验证表明,使用NIRS模型预测油酸、亚油酸和油浓度的结果准确可靠,这将为玉米油脂品质和含量遗传改良过程中大量育种样本的测定提供一种有用的方法。

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