Medicinal plants contain a large variety of chemical compounds in highly variable concentrations, so the quality control of these materials is especially complex. With this purpose, regulatory institutions have accepted chromatographic fingerprints as a valid tool to perform the analyses. In order to improve the results, separation conditions that maximise the number of detected peaks in these chromatograms are needed. This work reports the extension of a simulation strategy, based on global retention models previously developed for selected compounds, to all detected peaks in the full chromatogram. Global models contain characteristic parameters for each component in the sample, while other parameters are common to all components and describe the combined effects of column and solvent. The approach begins by detecting and measuring automatically the position of all peaks in a chromatogram, obtained preferably with the slowest gradient. Then, the retention time for each detected component is fitted to find the corresponding solute parameter in the global model, which leads to the best agreement with the measured experimental value. The process is completed by developing bandwidth models for the selected compounds used to build the global retention model based on gradient data, which are applied to all peaks in the chromatogram. The usefulness of the simulation approach is demonstrated by predicting chromatographic fingerprints for three medicinal plants with specific separation problems (green tea, lemon balm and linden), using several multi-linear gradients that lead to problematic predictions.
Keywords: Bandwidth models; Global retention models; Medicinal plants; Multi-linear gradient elution; Prediction of chromatographic fingerprints.
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