engleski

Principal component regression vs. partial linear squares regression in prediction modelling

Principal component regression (PCR) and partial least squares regression (PLSR) are mostly used multivariate analysis tools in the chemometrics. Challenge is to analyse superiority of one over another. As example herbal extract of melissa was used in this study. On melissa extract samples spectra analysis (Ultraviolet–visible spectroscopy, UVVIS and near infrared spectroscopy, NIR) and the content of total phenols (TP) were performed. The UV and NIR absorbance spectra of the aqueous extracts were gathered at three temperatures (T = 40, 60 and 80°C) in time interval from 0.5 to 90 min and were subject of PCR and PLS models. Models were tested for UV spectra range, for NIR spectra range and for the UV+NIR spectra range and the models refinement procedure and validation was performed by cross-validation. For the model efficiency analysis parameters as R-squared, root mean squared error of prediction RMSE, adjusted R2, Ratio of standard error of Performance to standard Deviation (RPD) and the Range Error Ratio (RER) were used. R2is describing how well the experimental data fit the statistical model. RMSEP is used as the measure of the average accuracy of the prediction. The accuracy of the model is also compared on the basis of adjusted R2 in order to regulate the number of model parameters for the available spectra. The R2, RER and RPD are dimensionless, meaning that they can be compared on the same basis between models for different constituents/properties allowing model efficiency assessment. Higher RPD and RER values suggest more accurate models. Values of RPD and RER less then 3 and 10, respectively, are an indication of qualitative models ; while models with higher values are considered even to be used in quantitative prediction. When the selected wavelength region of UV-VIS and NIR were used separately, the PLS produced slightly better results (R2UV-VIS=0.973, RPDUV- VIS=6.123, RERUV-VIS=22.236) with RMSE=4.800. For the combined spectral range of UV-VIS and NIR (325-1699 nm) the PCR model produced better results (R2=0.999, RPD=3.138, RER=13.200 with the RMSE=11, 877). To comment the superiority of one model over another is not an easy task, because the dimensionless parameters and error(s), RMSE, did not show exactly the same trend. The R2was higher for the model with higher RMSE. The major difference between PLSR and PCR was in obtaining the higher number of factors for PCR, which is not a significant problem.

PLSR ; PCR ; prediction modelling

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