Model-based data fitting analyses rely crucially on the choice of the correct model; model-free analyses allow insight into the data without prior chemical knowledge about the process. Model-free analysis is based on restrictions imposed on the results of the analysis. Only multivariate (for example, multi-wavelength) data are amenable to model-free analyses. The goal of the analysis is to decompose the matrix of data into a product of two physically meaningful matrices, usually into a matrix containing the concentration profiles of the components taking part in the chemical process, and a matrix that contains their absorption spectra (Beer–Lambert's law). If there are no model-based equations that quantitatively describe the data, model-free analyses are the only method of analysis.
Otherwise, the results of model-free analyses can guide the researcher in the choice of the correct model for a subsequent model-based analysis. The chapter also presents the important methods, principal component regression (PCR), and partial least-squares (PLS).
Bevington And Robinson Pdf To Excel Free
David Bevington
Both methods aim at predicting properties of samples based on spectroscopic information. The required information is extracted from a calibration set of samples with known spectrum and property. Previous chapter in volume. Next chapter in volume.