Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)
Olivieri, Alejandro C.;
Faber, Nicolaas M.;
Kalivas, John H.;
This paper gives an introduction to multivariate calibration from a
chemometrics perspective and reviews the various proposals to generalize the
well-established univariate methodology to the multivariate domain. Univariate
calibration leads to relatively simple models with a sound statistical underpinning.
The associated uncertainty estimation and figures of merit are thoroughly covered
in several official documents. However, univariate model predictions for unknown
samples are only reliable if the signal is sufficiently selective for the analyte of interest.
By contrast, multivariate calibration methods may produce valid predictions
also from highly unselective data. A case in point is quantification from near-infrared
(NIR) spectra. With the ever-increasing sophistication of analytical instruments
inevitably comes a suite of multivariate calibration methods, each with its
own underlying assumptions and statistical properties. As a result, uncertainty estimation
and figures of merit for multivariate calibration methods has become a
subject of active research, especially in the field of chemometrics.