The development of calibration models for spectroscopic data using principal component regression

The tutorial explains how to develop a calibration model for spectroscopic data analysis by Principal Component Regression (PCR), PCR basically consis ts of Principal Component Analysis (PCA) followed by a Multiple Linear Regr ession (MLR) step. Different diagnostics must however be implemented to det ect outliers, clustering tendency or nonlinearities in the data. The develo ped PCR model can further be optimized using UVE (uninformative variable el imination). The tutorial also explains how to handle replicates and how to perform different data preprocessings and/or pretreatments.