Zhang Lu-Da, Wang Tao, Yang Li-Ming, Zhao Li-Li, Zhao Long-Lian, Li Jun-Hui, Yan Yan-Lu
College of Science, China Agricultural University, Beijing 100094, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2005 Dec;25(12):1959-62.
In the present paper, 66 wheat samples were used as experimental materials, 33 of them were used for building the quantitative analysis model of protein content, and the rest composed the prediction set. Using Moore-Penrose matrix, we estimated directly the regression coefficients of the regression analysis model with Fourier transform near-infrared (FTNIR) whole spectroscopy. The samples of prediction set were analyzed, and the correlation coefficient is 0.979 9 between the prediction values of the near-infrared model and the standard chemical ones by Kjeldahl's method, and the average relative error is 1.76%. Using Moore-Penrose matrix, we can not only get the near-infrared spectroscopy analysis model's regression coefficients, but also know their contribution at every wavelength point. Consequently we can understand and explain the physical and chemical significance of the FTNIR whole spectroscopy regression model.
在本论文中，66个小麦样本用作实验材料，其中33个用于构建蛋白质含量的定量分析模型，其余的构成预测集。使用摩尔 - 彭罗斯矩阵，我们直接用傅里叶变换近红外（FTNIR）全光谱估计回归分析模型的回归系数。对预测集样本进行分析，近红外模型预测值与凯氏定氮法标准化学值之间的相关系数为0.979 9，平均相对误差为1.76%。使用摩尔 - 彭罗斯矩阵，我们不仅可以得到近红外光谱分析模型的回归系数，还能了解它们在每个波长点的贡献。因此，我们可以理解和解释FTNIR全光谱回归模型的物理和化学意义。
