Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares

摘要

Multivariate calibration aims to model the relation between a dependent variable, e.g. analyte concentration, and the measured independent variables, e.g, spectra, for complex mixtures. The model parameters are obtained in the form of a regression vector from calibration data by regression methods such as principal component regression (PCR) or partial least square (PLS). Subsequently, this regression vector is used to predict the dependent variable for unknown mixtures.