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Estimating the influence of experimental parameters on the prediction error of PLS calibration models based on Raman spectra
Author(s) -
Wolthuis Rolf,
Tjiang Gilbert C. H.,
Puppels Gerwin J.,
Bakker Schut Tom C.
Publication year - 2006
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.1475
Subject(s) - calibration , partial least squares regression , raman spectroscopy , set (abstract data type) , limit (mathematics) , observational error , mean squared prediction error , chemometrics , biological system , computer science , statistics , mathematics , machine learning , optics , physics , mathematical analysis , biology , programming language
Partial least squares (PLS) calibration is often the method of choice for making multivariate calibration models to predict analyte concentrations from Raman spectral measurements. In the development of such models, it is often difficult to assess beforehand what the prediction error will be, and whether instrumental or model factors limit the lower limit of the prediction error. Here, we present a method to assess the influence of experimental errors such as power fluctuations and spectral shifts, on the PLS prediction errors using simulated datasets. Assumptions that are implicit to PLS calibration and their implications with respect to the choice of experimental parameters for collecting a proper set of Raman spectra are discussed. The influence of various experimental parameters and signal pre‐processing steps on PLS prediction error is demonstrated by means of simulations. The results of simulations are compared with the outcome of PLS calibrations of an experimental dataset. Copyright © 2006 John Wiley & Sons, Ltd.