基于主成分回归的颜色与物质浓度辨识的研究

Identification of Color and Material Concentration Based on Principal Component Regression

针对颜色与物质浓度辨识问题,选取组胺浓度与颜色读数的数据,采用相关性分析和多元线性回归分析方法,建立物质浓度与颜色读数的多元线性回归模型。利用EViews软件对模型进行多重共线性检验,采用主成分回归方法对模型进行修正。研究结果表明,主成分回归模型可有效降低模型的多重共线性,同时对数据量较少的数据,通过降维可建立较好的回归模型。

In order to solve the problem about the color and material concentration identification,by selecting data of histamine concentration and color reading,we establish a multiple linear regression model of the material concentration and color readings through multiple linear correlation analysis and regression analysis method.The model was tested by EViews software,and then the model was modified by the principal component regression method.The results show that the model can effectively reduce the multicollinearity of principal component regression model,and establish a better regression model by reducing the dimension for the data amount of less data.