Estimating the influence of experimental parameters on the prediction error of PLS calibration models based on Raman spectra

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

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