of mobile-phase (B %) does not change during the separation. Isocratic elution is suitable for analysis of compounds with a relatively narrow range of polarity and hydrophobicity. In gradient elution, mobile phase strength (B %) increases during the separation. This means that sample retention is measured by k value decrease for each band as it migrates through the column [1-3]. Chromatographers are cautioned to avoid gradient elution when isocratic elution is possible to use. It is common knowledge that development and optimization of HPLC gradient methods can be time consuming and require many experiments [1,2]. Moreover, the transfer of a gradient elution method between columns, instruments and laboratories is notoriously more difficult than the transfer of an isocratic elution method [4,5]. Despite that gradient elution is a powerful method for separation and peak identification of many branches of chromatography provided that the optimum gradient profile, the profile that yields the best separation of the chromatographic peaks of a mixture of analytes can be easily determined [1-3, 6] The principal reason for using a gradient elution approach is a necessity to analyze samples containing solutes with a wide range of hydrophobicity [1-3, 6].As we mentioned before, optimization of gradient elution is a substantially more difficult task than optimization of isocratic conditions and can require many experiments, especially for multi-component samples [7][8][9][10]. Prediction of retention in gradient elution from structural formulae of analytes can potentially save time in method development, however, only a limited number of studies in this field have been published [11][12][13]. An approach described earlier requires many preliminary experiments to "train a column" and values of molecular descriptors which are mainly not known. Such an approach can be considered as theoretically interesting but without practical value [14][15][16]. The solvatic Abstract Application of the solvatic retention model of reversed-phase liquid chromatography was studied to predict retention of phenylisothiocyanate derivatives of amino acids from structural formulae and stationary and mobile phase properties. The gradient elution mode with methanol and acetonitrile aqueous mobile phases was used. It was shown that practically acceptable prediction or retention time values can be achieved after the first approximation step when experimental data of one run are used. The zero approximation level predictions-from structural formulae, column and mobile phase properties can be used as a "first guess" method from which further optimization can begin.
We report our experience with highly polar and charged analyte retention parameter prediction for a reversed-phase high-performance liquid chromatographic method. The solvatic retention model has been used to predict retention of phenylisothiocyanate derivatives of 25 natural amino acids under gradient elution conditions. Retention factors have been calculated from molecular parameters of analyte structures and from the column and eluent characteristics. A step-by-step method which includes the first guess prediction of initial conditions from structural formula and fine tuning of the retention model parameters using data from successive runs can substantially save method development time.
Until now, cheese peptidomics approaches have been criticised for their lower throughput. Namely, analytical gradients that are most commonly used for mass spectrometric detection are usually over 60 or even 120 min. We developed a cheese peptide mapping method using nano ultra-high-performance chromatography data-independent acquisition high-resolution mass spectrometry (nanoUHPLC-DIA-HRMS) with a chromatographic gradient of 40 min. The 40 min gradient did not show any sign of compromise in milk protein coverage compared to 60 and 120 min methods, providing the next step towards achieving higher-throughput analysis. Top 150 most abundant peptides passing selection criteria across all samples were cross-referenced with work from other publications and a good correlation between the results was found. To achieve even faster sample turnaround enhanced DIA methods should be considered for future peptidomics applications.
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