Wang Wei-Dong, Gu Yun-Hong, Qin Guang-Yong, Huo Yu-Ping
Henan Province Key Laboratory of Ion Beam Bio-engineering, Zhengzhou University, Zhengzhou 450052, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Apr;27(4):697-701.
In the present article, the measurement of intact wheat seeds' protein content with near infrared reflentance spectroscopy(NIRS) was studied. The wavelength range of 1 100-2 498 nm was used. The distinguishability of NIRS machine was 2 nm. Firstly the representative wheat samples with different protein contents were selected and the original spectra of wheat were obtained using NIRS machine. Then through scatter correction and maths treatment, spectrum noise were decreased. Finally regression methods used multiple linear regression, principal component regression and modified partial least squares respectively. The result showed that after optimizing all the factors, the best calibration model of equation was chosen using "first derivative" +"Standard Multiplicative Scatter Correction, SMSC"+"Modified Partial Least Squares, MPLS". RSQ, SECV and 1-VR of the obtained calibration model were 0. 94, 0. 42 and 0. 87 respectively. A set of representative individual wheat samples were uesed to check the model, and prediction coefficient of determination was 0.88. Protein content of wheat could be preidicted quickly and scathelessly by using the NIRS measurement. It was feasible to be used in early generation selection in wheat quality breeding process.
本文研究了利用近红外反射光谱法(NIRS)测定完整小麦种子的蛋白质含量。使用的波长范围为1100 - 2498nm。NIRS仪器的分辨率为2nm。首先,选取具有不同蛋白质含量的代表性小麦样品,利用NIRS仪器获取小麦的原始光谱。然后通过散射校正和数学处理,降低光谱噪声。最后分别采用多元线性回归、主成分回归和改进偏最小二乘法等回归方法。结果表明,在对所有因素进行优化后,采用“一阶导数”+“标准多元散射校正,SMSC”+“改进偏最小二乘法,MPLS”选择了最佳的校准模型方程。所得校准模型的决定系数(RSQ)、交互验证均方根误差(SECV)和交互验证相关系数(1-VR)分别为0.94、0.42和0.87。使用一组具有代表性的单个小麦样品对模型进行检验,预测决定系数为0.88。利用NIRS测量可以快速、无损地预测小麦的蛋白质含量。在小麦品质育种过程中用于早代选择是可行的。