Multi-objective optimisation using evolutionary algorithms: its application to HPLC separations

Cela, R.; Martinez, Jhonattan G.; González-Barreiro, C.; Lores, Marta

doi:10.1016/j.chemolab.2003.07.001

Cited by 37 publications

(13 citation statements)

References 20 publications

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“…In this plot, each component is associated with a different color and filled symbols correspond to validation runs. Because the retention model appeared satisfactory, an optimization process using Pareto optimality [ 32 ] was launched to establish the optimal elution conditions. Figure 6 compares the proposed optimal elution, showing the simulated chromatogram (A), the UV maxplot (B), and the MS TIC (C).…”

Section: Resultsmentioning

confidence: 99%

“…In this plot, each component is associated with a different color and filled symbols correspond to validation runs. Because the retention model appeared satisfactory, an optimization process using Pareto optimality [32] was launched to A B C Fig. 6 Experimental verification of the optimal separation proposed in case study B.…”

Section: Case Study Bmentioning

confidence: 99%

“…This algorithm provides several advantages as compared to regular genetic algorithms used in previous studies [ 31 ] both in convergence speed, population size, and calculation speed, so it is possible to produce optimized results in a few minutes even for rather complex samples. The multi-objective Pareto approach [ 32 ] is the recommended option as will be explained in the “Results and discussion” section, although more classical approaches are also available.…”

Section: Theorymentioning

confidence: 99%

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A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

Cela

Triñanes

Cobas³

2021

Anal Bioanal Chem

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A new strategy for the computer-assisted methods development in the reversed-phase liquid chromatographic separations of unknown sample mixtures has been developed using the latent spectral information in chromatogram raw data files of appropriately designed experiments, rather than resorting to elemental information functions (e.g., the number of peaks in chromatograms or similar criteria). The strategy developed allows unification of the approach for samples of both known and unknown composition and, thus, provide a general strategy for computer-aided tools in the chromatography laboratory. The operation principle of this strategy departs from extracting the spectra of components in the mixture chromatograms by resorting to multivariate curve resolution-alternating least squares (MCR-ALS). This technique allows the estimation of the true spectra for the individual components except when they have identical spectra or are fully overlapped. Thus, a convenient experimental design will try to perform separations of the sample mixture having at least partial resolution of components in some runs. This will allow estimating the spectra of components and, then, assign these components to the peaks in each run chromatogram. In this way, a retention model can be built for each component so computerized optimization process can be developed to provide the chromatographer with the best possible separation programs. Following this approach, strategies for sample mixtures of known and unknown composition are only different in the need of an initial spectrum discovery process for unknown mixtures and therefore a real general approach for the computer-assisted LC methods development is now available for the first time.

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Section: Resultsmentioning

confidence: 99%

“…In this plot, each component is associated with a different color and filled symbols correspond to validation runs. Because the retention model appeared satisfactory, an optimization process using Pareto optimality [32] was launched to A B C Fig. 6 Experimental verification of the optimal separation proposed in case study B.…”

Section: Case Study Bmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

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A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

Cela

Triñanes

Cobas³

2021

Anal Bioanal Chem

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“…The first attempt to solve this problem was based on the theoretical advances in chromatography, which allowed the experimental response to be predicted by controlling and modifying the chromatographic parameters 11. Subsequently, computer‐assisted methodologies were suggested as a more sophisticated method for optimization purposes 12, 13…”

Section: Introductionmentioning

confidence: 99%

A sequence optimization strategy for chromatographic separation in reversed‐phase high‐performance liquid chromatography

Yuan

2009

AIChE Journal

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A sequence optimization strategy combining an artificial neural network (ANN) and a chromatographic response function (CRF) for chromatographic separation in reversed-phase high-performance liquid chromatography has been proposed. Experiments were appropriately designed to obtain unbiased data concerning the effects of varying the mobile phase composition, flow-rate, and temperature. The ANN was then used to simultaneously predict the resolution and analysis time, which are the two most important features of chromatographic separation. Subsequently, a CRF consisting of resolution and analysis time was used to predict the optimum operating conditions for different specialized purposes. The experimental chromatograms were consistent with those predicted for given conditions, which verified the applicability of the method. Furthermore, the proposed optimization strategy was applied to literature data and very good agreement was obtained. The results show that a strategy of sequential combination of ANN and CRF can provide a more flexible and efficient optimization method for chromatographic separation.

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“…Chromatographic conditions have been thoroughly optimized using experimental design and multicriteria functions for reaching a suitable compromise regarding resolution of close peaks and analysis time. Similar approaches have been considered elsewhere for facilitating the optimization of pretreatment and separation conditions and extracting relevant information from a limited number of experiments [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Reversed-phase liquid chromatographic method with spectrophotometric detection for the determination of antiretroviral drugs

Checa

Oliver

Hernández-Cassou

et al. 2008

Analytica Chimica Acta

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Multi-objective optimisation using evolutionary algorithms: its application to HPLC separations

Cited by 37 publications

References 20 publications

A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

A sequence optimization strategy for chromatographic separation in reversed‐phase high‐performance liquid chromatography

Reversed-phase liquid chromatographic method with spectrophotometric detection for the determination of antiretroviral drugs

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