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Chromatographic scince series - Cazes J.

Cazes J. Chromatographic scince series - Marcel Dekker, 1996. - 1098 p.
ISBN 0-8247-9454-0
Download (direct link): сhromatography1996.pdf
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and TAB volume) optimization in reversed-phase HPTLC. The results are given in Figure 5. Fifty four iterative processes are performed by the computer. The AhRf= 6.55 is the highest value for the worst separation pair of the spots. The optimum condition is Xs = 0.20 and TAB = 1.66 ml.
A similar procedure was applied to the analysis of twelve PTH-amino acids for two-factor (mobile phase composition and impregnate ion concentration) optimization in normal-phase HPTLC. The predicted and experimental results in reversed-phase HPTLC and normal-phase HPTLC correlated closely.
Comparing the experiments with the advanced simplex method and the general sequential simplex method, the former requires only ten while the latter needs about fifty-four (RP-HPTLC) or forty (NP-HPTLC) experiments. In addition, the advanced simplex method can select repeatedly initial simplex experiments from preliminary experiments without any additional experiments. Therefore the advanced simplex method has distinct advantages over the general sequental simplex method for two-factor optimization in HPTLC.
C. The PRISMA method
The PRISMA model method was introduced by Nyiredy and co-workers [19] for optimization of the mobile phase in reversed-phase HPLC. It has been effectively used in planar chromatography [20-23]. The PRISMA model is a structured trial and error approach and is a three-dimensional model, correlating the solvent strength and the selectivity of mobile phases. The solvent selection is performed according to Snyders solvent classification [24]. With this optimization model, the most advantageous mobile phase composition may be systematically elaborated, and from one to four solvents can be combined to achieve a suitable separation.
Optimization
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Figure 5 Mixture design simplex optimization for five benzoic acid derivatives in RP-HPTLC. (Reprinted with permission from Ref. 18.)
The PRISMA model has three parts: an irregular frustum, a regular middle part, and a platform (Figure 6). The three top corners of the model represent the selected three individual solvents which can be diluted with hexane. The solvent strength is represented by the height of the prism (STA,STB,STc)-points along the edges stand for combination of two solvents, points on the sides for combination of three, and the point in the interior of the prism for mixtures of four solvents.
The optimization steps with either polar or nonpolar samples are rather similar. In the case of the nonpolar sample, the initial solvent composition corresponds to the center of the triangular top face of the regular prism; this composition is then diluted to bring all sample components into the Rf range
0.2-0.8. The solvent strength is then maintained and an additional three chromatograms are run at solvent compositions corresponding to selectivity points near the apices of the triangle, which should be near the extremes of selectivity for the solvent system. These initial runs are then used to choose selectivity points for further chromatograms until the best solvent composition is located. During the final stages of the optimization the solvent strength may be fine-tuned by adjusting the hexane concentration. If the best chromatogram does not exhibit adequate resolution, one or more of the primary solvents can be changed and the optimization procedure repeated. If none of the chromatograms at the first four selectivity points (i.e., at the center of the triangle as well as the points near the three apices) is better than the best of the four corresponding chromatograms with the previous system, further solvent systems should be investigated. In the case of polar samples, the upper face of the frustum is utilized and the optimization proceeds in a very similar way.
The performance of the optimization design is demonstrated with the separation of mixtures of naturally occurring compounds.
88
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Figure 6 The PRISMA optimization model.
The important difference between the statistical mixture design and the PRISMA method is that the former yields a computer-assisted optimum solvent composition whereas the latter relies on structured trial and error. In TLC, the PRISMA method is a viable alternative because the time to prepare and evaluate each solvent composition is small and several different compositions can be evaluated simultaneously with several development systems. The PRISMA is also very powerful for the selection of mobile phase in over-pressured layer chromatography.
D. Mixture Design Statistical Technique
The mixture design statistical method has been successfully introduced in HPLC by Glajch and co-workers [25]. Because the diagram is equivalent to the physical overlapping of individual diagrams for all possible solute pairs, the name overlapping resolution map (ORM) was used. This is one of the most important methods of solvent optimization. Snyder [24] classified solvents into eight group, depending on their relative ability to function as a proton acceptor (Xe), a proton donor ( or a strong dipole interactor (X). These three coordinates of a solvent determine its position in a triangle. Eight groups are thus obtained, each containing solvents of roughly the same selectivity (Figure 7).
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