Application of experimental design and radial basis function neural network to the separation and determination of active components in traditional Chinese medicines by capillary electrophoresis

Multivariate optimization of capillary electrophoresis methods: a critical review

In this article a review on the recent applications of multivariate techniques for optimization of electromigration methods, is presented. Papers published in the period from August 2007 to February 2013, have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.

Optimization of micellar electrokinetic capillary chromatography method using central composite design for the analysis of components in Yangwei granule

Central composite design (CCD), together with multiple linear regression, was successfully used to optimize the electrophoretic buffer system of micellar electrokinetic capillary chromatography (MEKC) for the determination of albiflorin, paeoniflorin, liquiritin, and glycyrrhizic acid in the traditional Chinese medicine (TCM) prescription, Yangwei granule. Concentrations of sodium deoxycholate (SDC) and borate, and proportions of ammonia, acetonitrile, and methanol were optimized. The total resolutions of peaks between the analytes and their adjacent peaks in real samples were integrated into the evaluation index of separation efficiency. The optimum electrophoretic buffer contained 80 mmol/L SDC, 20 mmol/L borate, 5% (v/v) methanol, 0.5% (v/v) ammonia, and 5% (v/v) acetonitrile. The correlation coefficients (R(2)) between the peak areas and the corresponding concentrations of analytes were greater than 0.9956. The limits of detection (LODs) (S/N=3) of the analytes were 0.97-4.00 μg/ml. The results indicate the superiority of CCD in optimizing the separation conditions of complex samples such as TCM prescriptions.

Studies on the non-covalent interactions between cyclodextrins and aryl alkanol piperazine derivatives by mass spectrometry and fluorescence spectroscopy

The non-covalent complexes of alpha- and beta-cyclodextrins (alpha-, beta-CDs) with two aryl alkanol piperazine derivatives (Pipe I and Pipe II) have been studied by electrospray ionization mass spectrometry (ESI-MS) and fluorescence spectroscopy. The ESI-MS experimental results demonstrated that Pipe I can conjugate to beta-CD and form 1:1 or 1:2 stoichiometric non-covalent complexes, and Pipe II can only form 1:1 complexes with alpha- or beta-CD. Fluorescence spectra indicated that the fluorescence intensities of Pipe I and Pipe II can be enhanced by increasing the content of beta-CD. The mass spectrometric titration experiments showed that the dissociation constants K(d1) were 5.77 and 9.52 x 10(-4) mol L(-1) for the complexes of alpha-CD with Pipe I and Pipe II, respectively, revealing that the binding of alpha-CD-Pipe I was stronger than alpha-CD-Pipe II. The K(d1) and K(d2) values were 9.81 x 10(-4) mol L(-1) and 1.11 x 10(-7) (mol L(-1))(2) for 1:1 and 1:2 complexes of Pipe I with beta-CD, respectively. The K(d) values obtained from fluorescence spectroscopy were in agreement with those from ESI-MS titration.

Experimental design in chemistry: A tutorial

In this tutorial the main concepts and applications of experimental design in chemistry will be explained. Unfortunately, nowadays experimental design is not as known and applied as it should be, and many papers can be found in which the "optimization" of a procedure is performed one variable at a time. Goal of this paper is to show the real advantages in terms of reduced experimental effort and of increased quality of information that can be obtained if this approach is followed. To do that, three real examples will be shown. Rather than on the mathematical aspects, this paper will focus on the mental attitude required by experimental design. The readers being interested to deepen their knowledge of the mathematical and algorithmical part can find very good books and tutorials in the references [G.E.P. Box, W.G. Hunter, J.S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons, New York, 1978; R. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons, New York, 1978; R. Carlson, J.E. Carlson, Design and Optimization in Organic Synthesis: Second Revised and Enlarged Edition, in: Data Handling in Science and Technology, vol. 24, Elsevier, Amsterdam, 2005; J.A. Cornell, Experiments with Mixtures: Designs, Models and the Analysis of Mixture Data, in: Series in Probability and Statistics, John Wiley & Sons, New York, 1991; R.E. Bruns, I.S. Scarminio, B. de Barros Neto, Statistical Design-Chemometrics, in: Data Handling in Science and Technology, vol. 25, Elsevier, Amsterdam, 2006; D.C. Montgomery, Design and Analysis of Experiments, 7th edition, John Wiley & Sons, Inc., 2009; T. Lundstedt, E. Seifert, L. Abramo, B. Thelin, A. Nyström, J. Pettersen, R. Bergman, Chemolab 42 (1998) 3; Y. Vander Heyden, LC-GC Europe 19 (9) (2006) 469].