3 years ago

Characterization of chromatographic peaks using the linearly modified Gaussian model. Comparison with the bi-Gaussian and the Foley and Dorsey approaches

To characterize column performance in liquid chromatography, several parameters must be obtained from experimental data. These parameters can be computed through the numerical integration of the net signal to calculate the moments after subtraction of the baseline. This requires the establishment of the peak integration limits. The whole process introduces significant uncertainty. For this reason, several alternative procedures have been proposed to measure the area, mean time and variance, based on the assumption that the chromatographic peak can be described with a mathematical function. This allows the calculation of the peak position and variance making use of the values of the experimental half-widths. In this work, the linear modified Gaussian model is used to derive several equations for the evaluation of the associated moments. Affordable equations for the calculation of the area, mean time, variance and efficiency are provided, using the half-width values at 10% peak height. The behaviour of experimental peaks obtained under a large variety of experimental conditions is examined to verify the validity of the proposed equations. The values of the peak parameters are compared with those calculated based on the bi-Gaussian model, and the exponentially modified Gaussian model using the equations developed by Foley and Dorsey. The bi-Gaussian model offered the best quantifications for the mean time. The Foley and Dorsey approach gave rather satisfactory results for the area and the best results for the variance and efficiency for tailing peaks of small asymmetry. The LMG approach gave better evaluation of the area for peaks showing small asymmetry, and satisfactory values for the mean time, variance and efficiency in the whole range of asymmetries found in liquid chromatography.

Publisher URL: www.sciencedirect.com/science

DOI: S0021967317311317

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