Chemometric Methods in Capillary Electrophoresis 2009
DOI: 10.1002/9780470530191.ch2
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Experimental Design in Method Optimization and Robustness Testing

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Cited by 10 publications
(36 citation statements)
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“…(1) (2) in which ΣY(1), ΣY(0), and ΣY(-1) represent the sum of the responses (resolution between enantiómeros, Rs) where the factors x (CD type, % CD, pH and % MeOH) are at level 1, 0 and -1, respectively (Table 1), with "N" representing the number of design experiments [29,[31][32] for a 3 levels, 4 factors fractional factorial design (3 4-2 ) used here N= 9 experiments. The aim of this evaluation is to select the experimental conditions that lead to the best separation (i.e.…”
Section: Calculationsmentioning
confidence: 99%
“…(1) (2) in which ΣY(1), ΣY(0), and ΣY(-1) represent the sum of the responses (resolution between enantiómeros, Rs) where the factors x (CD type, % CD, pH and % MeOH) are at level 1, 0 and -1, respectively (Table 1), with "N" representing the number of design experiments [29,[31][32] for a 3 levels, 4 factors fractional factorial design (3 4-2 ) used here N= 9 experiments. The aim of this evaluation is to select the experimental conditions that lead to the best separation (i.e.…”
Section: Calculationsmentioning
confidence: 99%
“…CE is suitable for the use of experimental design in which the experimental conditions could be varied from one experiment to another. Up to date, there are only a few studies that report the use of experimental designs in the field of chiral enantioseparation in CE . The separation strategies are therefore valuable since they should propose a limited set of experimental conditions, which maximize the probability to separate numerous pharmaceuticals with different molecular structures and properties .…”
Section: Introductionmentioning
confidence: 99%
“…QbD has become a significant model for the pharmaceutical industries and defined in the International Conference on Harmonization (ICH) regulation on pharmaceutical development as ''a systematic approach to the development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.'' [2] For method optimization, either sequential optimization methods, using simplex approaches, [3,4] or simultaneous optimization strategies, using response-surface designs, [4][5][6] are reported. The main difference between their applications is that for a response-surface design the experimental design domain, defined by the factor levels examined, is expected to contain the optimum, whereas a sequential optimization method can be applied in situations where the experimental domain containing the optimum result is not previously known.…”
Section: Introductionmentioning
confidence: 99%