New approaches based on modified Gaussian models for the prediction of chromatographic peaks

The description of skewed chromatographic peaks has been discussed extensively and many functions have been proposed. Among these, the Polynomially Modified Gaussian (PMG) models interpret the deviations from ideality as a change in the standard deviation with time. This approach has shown a high accuracy in the fitting to tailing and fronting peaks. However, it has the drawback of the uncontrolled growth of the predicted signal outside the elution region, which departs from the experimental baseline. To solve this problem, the Parabolic-Lorentzian Modified Gaussian (PLMG) model was developed. This combines a parabola that describes the variance change in the peak region, and a Lorentzian function that decreases the variance growth out of the peak region. The PLMG model has, however, the drawback of its high flexibility that makes the optimisation process difficult when the initial values of the model parameters are far from the optimal ones. Based on the fitting of experimental peaks of diverse origin and asymmetry degree, several semiempirical approaches that make use of the halfwidths at 60.65% and 10% peak height are here reported, which allow the use of the PLMG model for prediction purposes with small errors (below 2–3%). The incorporation of several restrictions in the algorithm avoids the indeterminations that arise frequently with this model, when applied to highly skewed peaks.

Figure optionsDownload as PowerPoint slideHighlights
► Modified Gaussian models expressed as a function of experimental halfwidths.
► Enhancement of peak models through the application of restrictions.
► Peak models checked on chromatographic peaks of diverse origin and asymmetry.
► Increased reliability of the peak models in tasks requiring peak fitting and prediction.
► Parabolic-Lorentzian Gaussian improved applicability for optimisation of peak resolution.