以66个小麦样品为试验材料, 研究岭回归方法在近红外光谱定量分析中的应用。 用44个小麦样品的近红外光谱数据建立测定蛋白质含量的近红外-岭回归模型, 预测其余22个小麦样品的蛋白质含量。 预测结果与凯氏定氮法分析结果(化学分析值)的平均相对误差为1.518%, 与偏最小二乘法(PLS)预测结果进行比较, 显示岭回归方法可用于近红外光谱定量分析; 进一步, 为了减少无关信息对定量分析模型预测能力的干扰, 一种有效的方法就是进行波长信息的选择。 从1 297个波长点中优选出4个波长点, 利用这4个波长点处的光谱信息建立近红外-岭回归模型预测22个样品的蛋白质含量, 预测结果与凯氏定氮法分析结果之间的平均相对误差为1.37%, 相关系数达到0.981 7。 结果表明岭回归方法从大量光谱信息中筛选出了最重要的波长信息、 不仅简化了模型, 有效的减少了光谱信息共线性的干扰, 而且对特定分析选择出适用的波长对指导设计专用近红外定量分析仪器亦有实际意义。

In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl’s values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model’s predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl’s values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.