2006
DOI: 10.1016/j.chroma.2006.01.005
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Multilinear gradient elution optimisation in reversed-phase liquid chromatography using genetic algorithms

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Cited by 49 publications
(31 citation statements)
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“…Figure 4 shows a chromatogram of 18 underivatized amino acids and related compounds recorded by using three detectors in series and under optimal gradient conditions found in the ref. [32]. The MeCN gradient profile used is shown in the UV detected chromatogram (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Figure 4 shows a chromatogram of 18 underivatized amino acids and related compounds recorded by using three detectors in series and under optimal gradient conditions found in the ref. [32]. The MeCN gradient profile used is shown in the UV detected chromatogram (Fig.…”
Section: Resultsmentioning
confidence: 99%
“…In 2006, Nikitas and coworkers reported on the use of Genetic Algorithms to optimize multi-linear gradients [20,21]. However, this optimization approach requires a priori selection of initial parameters such as the number of generations, the population size and the probability of mutation [20]. Concha-Herrara and coworkers investigated the benefit of including more and more segments using a time consuming grid search as optimization strategy.…”
Section: Introductionmentioning
confidence: 99%
“…This software combines chromatogram simulation and fast Monte-Carlo optimization to optimize multi-linear gradients [19]. In 2006, Nikitas and coworkers reported on the use of Genetic Algorithms to optimize multi-linear gradients [20,21]. However, this optimization approach requires a priori selection of initial parameters such as the number of generations, the population size and the probability of mutation [20].…”
Section: Introductionmentioning
confidence: 99%
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“…For example, Nikitas et al [7] optimized a stepwise gradient so as to maximize differences in retention time of different solutes. The same authors later generalized this optimization problem to multilinear elution and minimization of a predefined cost function [8]. Nonlinear gradient shapes were also considered.…”
mentioning
confidence: 99%