27 No. 3
Uncertainty Estimation and Figures of Merit for Multivariate Calibration
With the ever-increasing sophistication of analytical instruments, multivariate calibration methods are continually evolving, each with its own underlying assumptions and statistical properties. The main purpose of these methods is to produce valid predictions from highly unselective data (e.g., the quantification from near-infrared spectra). A wide variety of multivariate methods have been developed, broadly classified in terms of the tensorial order of the instrumental data. Important conceptual differences exist between first-order methods employing vector data, and second-order methods using matrix data, particularly since the latter make possible the quantification in the presence of unknown interferents. This is also reflected in the approaches followed for the estimation of figures of merit.
While univariate calibration leads to relatively simple models, and the associated uncertainty estimation and figures of merit are thoroughly covered in several official documents, multivariate calibration does not enjoy a similar status in this regard. Uncertainty estimation and figures of merit for multivariate calibration methods have become subjects of active research, especially in the field of chemometrics.
This work is intended as an introduction to multivariate calibration from a chemometrics perspective and as a review of the various proposals to generalizing the well-established univariate methodology to the multivariate domain.
Uncertainty and figures of merit are subjects of interest to IUPAC’s Analytical Chemistry Division, as established within the general aims of the Division, and also in the terms of reference of its Interdivisional Working Party on Harmonization of Quality Assurance.
For more information and comments contact the Task Group Chairman Alejandro C. Olivieri <firstname.lastname@example.org>.
last modified 7 April 2005.
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