The results obtained by implementing Principal Component Regression (PCR) according to three different criteria for choosing principal components (PCs), and those provided by Partial Least-Squares Regression (PSLR), in the determination of the active compound in a pharmaceutical preparation by near infrared diffuse reflectance spectroscopy are compared. The PCR-top down criterion used is commonly implemented in commercially available software: it selects consecutive PCs beginning with that possessing the largest eigenvalue. The other two criteria used do not assume the PCs with the largest eigenvalues to be the best predictors for the response variable; rather, the PCR-correlation criterion chooses only those PCs exhibiting the highest correlation with the response variable, and the PCR-best subset criterion selects those that provide the lowest predicted residual sum of squares (PRESS) for an external prediction set. All the calibration methods tested exhibited a similar predictive ability (prediction errors ranged from 1.34% to 1.49%); however, the number of PCs used in the regression varied among them. The PLSR technique did not excel the methods based on selecting the best PCs for regression. Also, the PCR-correlation and PCR-best subset methods provided the same results and used fewer PCs than the PCR-top down method.
|Journal||J. Near Infrared Spectrosc.|
|Publication status||Published - 1 Jan 1997|
- Near infrared spectroscopy
- Partial least-squares regression (PLSR)
- Principal component regression (PCR)
- Principal component selection