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Ortiz, M.C.; Herrero, Ana M.; Sanllorente, S.; Reguera, Celia

doi:10.1016/j.talanta.2004.06.031

Cited by 14 publications

(14 citation statements)

References 11 publications

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“…Then, the real functional relationship (response function) between the analytical response and the significant factors is estimated, obtaining from it the optimum values for these factors. Since previous experiments showed that changes on the selected variables enhanced the CL signal of one of the analytes while others were decreased, in order to reach the optimum values for the studied parameters for which the maximum responses are achieved simultaneously for the three studied pesticides, we have applied the desirability function [29] considering the enhancement on CL signal and subsequently on the peak area for each analyte. The maximum of this function shows the optimum CL conditions for all compounds.…”

Section: Optimization Of the Post-column CL Reactionmentioning

confidence: 99%

Determination of N-methylcarbamate pesticides in water and vegetable samples by HPLC with post-column chemiluminescence detection using the luminol reaction

Huertas-Pérez

Garcı́a-Campaña

2008

Analytica Chimica Acta

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Section: Optimization Of the Post-column CL Reactionmentioning

confidence: 99%

Determination of N-methylcarbamate pesticides in water and vegetable samples by HPLC with post-column chemiluminescence detection using the luminol reaction

Huertas-Pérez

Garcı́a-Campaña

2008

Analytica Chimica Acta

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“…Value 1 is assigned to the responses that are of lower importance, and the higher the value of r, the greater is the importance of the response. The greatest benefit of the desirability functions is reducing the multicriteria problem to a single criterion problem that can be optimized in a simpler way [21,[24][25][26].…”

Section: Methodsmentioning

confidence: 99%

Chemometrically assisted RP-HPLC method development for efficient separation of ivabradine and its eleven impurities

Tomić

Ivković

Oljačić

et al. 2020

Acta Chromatographica

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The aim of this study was to develop a novel reversed-phase high-performance liquid chromatography (RP-HPLC) method for efficient separation of ivabradine and its 11 impurities. Similar polarity of impurities in the sample mixture made method optimization challenging and accomplishable only when different chemometric tools, such as principal component analysis (PCA), Box–Behnken design (BBD), and desirability function as a multicriteria approach, were employed. The presence of 3 positional isomers (impurities III, V, and VI), keto–enol tautomerism of impurity VII, and diastereoisomers of impurity X made separation of this complex mixture even more challenging. Chromatographic retention parameters obtained with the mobile phase consisting of 30 mM phosphate buffer and acetonitrile (80:20, v/v) on four different RP-HPLC columns at varying pH values (3.0, 4.0, and 5.0) were subjected to the PCA analysis to select the column with the most appropriate selectivity. Then the column temperature, pH of the aqueous component of mobile phase, phosphate buffer molarity and the organic solvent content in the mobile phase were estimated employing BBD. Valid and reliable mathematical models towards resolution of twelve critical peak pairs were obtained. After determination of the desirability making criteria for all responses, desirability functions were established and used in optimization. The proposed optimal chromatographic conditions included the Zorbax Eclipse Plus C18 chromatographic column (100 × 4.6 mm, 3.5 μm), the column temperature of 34 °C, the mobile phase flow rate of 1.6 mL min−1 and the UV detection at 220 nm. The mobile phase consisted of the 28 mM phosphate buffer at pH 6.0 and acetonitrile (85:15, v/v). Separation of one pair of positional isomers was not achieved, so methanol was added to the organic part of mobile phase in small increments with the optimal ratio of methanol to acetonitrile 59:41, v/v. The overall organic component of the mobile phase also increased to 18%, accelerating the chromatographic analysis.

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“…The former option has been extensively considered, above all in the form of the definition of a desirability function, in several analytical applications in different analytical techniques such as stripping voltammetry [4], gas chromatography mass spectrometry [5,6], liquid chromatography-tandem mass spectrometry [7], flow analysis [8] or flow injection analysis (FIA) [9]. In short, this method consists of using a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors.…”

Section: Introductionmentioning

confidence: 99%

Improving the visualization of the Pareto-optimal front for the multi-response optimization of chromatographic determinations

Ortiz¹,

Sarabia²,

Sánchez³

et al. 2011

Analytica Chimica Acta

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Untitled

Cited by 14 publications

References 11 publications

Determination of N-methylcarbamate pesticides in water and vegetable samples by HPLC with post-column chemiluminescence detection using the luminol reaction

Determination of N-methylcarbamate pesticides in water and vegetable samples by HPLC with post-column chemiluminescence detection using the luminol reaction

Chemometrically assisted RP-HPLC method development for efficient separation of ivabradine and its eleven impurities

Improving the visualization of the Pareto-optimal front for the multi-response optimization of chromatographic determinations

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