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 samples
to select the optimal subset, which is then submitted to
chemical analysis and calibration. The subset is
selected
to provide a low variance of the regression coefficients.
The methodology has been applied to UV−visible spectroscopy data to determine Ca2+ in water and
near-IR
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-mean-squared error
of cross-validation (RMSECV) is found when comparing
the new methodology with the results of the regression
models that use the complete set of calibration samples
and PCR. The number of analyzed samples in the
calibration set can be reduced by up to 50%, which
represents a considerable reduction in costs.