Abstract
Karstang, T.V. and Kvalheim, O.M., 1991. Comparison between three techniques for background correction in quantitative analysis. Chemometrics and Intelligent Laboratory Systems, 12: 147–154. The three background techniques compared in this work all take as their starting point a principal component (PC) model that describes the calibration space. The PC model is then combined with a function that describes the spectrum of the background constituents. Three data sets are analyzed, of which two are normalized to constant sum for the concentrations. The results using (1) curve fitting (CF), (2) iterative target transformation factor analysis, and (3) local curve fitting (LCF), indicate that the curve fitting techniques (LCF and CF) give smallest prediction errors.
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