Selection of Principal Component in Principal Component-spectrophotometry
Zhong Leiming, Jiang Peidong, Fu Shimi (Institute of Biophysics,Chinese Academy of Sciences, Beijing 100101)
Principal component analysis is widely applied to the multivariate calibration. In principal component-spectrophotometry, the first several principal components regress with concentration to get regression coffecient. But the first principal component may not be a best linear correlated with concentration. We use scan algorithms method for the choice of several principal components that are best linear correlated with concentration from a lot of principal components. These principal components regress with concentrations to get regression coffecients. These regression coffecients are applied to the prediction of concentration of unknown sample. A program written in Turbo BASIC has been applied to the quantitative analysis of the Fourier transform near infrared diffuse reflectance spectroscopy of wheat sample and UV spectroscopy of six amino acids mixture with satisfactory results.
【CateGory Index】： O657.3