Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Abstract

This report reviews recent literature on the application of multivariate calibration techniques to both first- and second-order data, aimed at the analytical determination of analytes of interest or sample properties in a variety of industrial, pharmaceutical, food, and environmental samples, including examples of process control. The most used data processing tools are briefly described, with emphasis on the advantages that can be obtained by applying specific combinations of multivariate data and algorithms. The main focus is on works devoted to first-order data (i.e., spectra, chromatograms, etc.) combined with partial least-squares regression, which has become the standard for this type of analytical research. A brief discussion on recent work on second-order data and algorithms is also included, as this field is rapidly growing, although at present it does not show, the general applicability of the first-order counterparts.

Simultaneous determination of food colorants in liquid samples by UV‐Visible spectroscopy and multivariate data analysis using a reduced calibration matrix

“…However, PLS demands a big and representative calibration set. In any case, the possible interferences must be included in the calibration set to build a robust PLS model.…”