SELECTION OF THE BEST CALIBRATION SAMPLE SUBSET FOR MULTIVARIATE REGRESSION

This paper discusses a methodology for selecting the minimum number of calibration samples in principal component regression (PCR) analysis. The method uses only the instrumental responses of a large set of sam ples to select the optimal subset, which is then submitted to chemical analysis and calibration. The subset is selected to provide a low var iance of the regression coefficients. The methodology has been applied to UV-visible spectroscopy data to determine Ca2+ in water and near-I R spectroscopy data to determine moisture in corn. In both cases, the regression models developed with a reduced number of samples provided accurate results. As far as precision is concerned, a similar root-mea n-squared error of cross-validation (RMSECV) is found when comparing t he new methodology with the results of the regression models that use the complete set of calibration samples and PCR. The number of analyze d samples in the calibration set can be reduced by up to 50%, which re presents a considerable reduction in costs.