Optimization of analytical methods

Chemometric techniques have been frequently used for optimization of analytical methods, as they are faster, more economical and effective and allow more than one variable to be optimized simultaneously. Among these, two level fractional factorial design (2 ) is used mainly for preliminary evaluation of the significance of the variables and its interactions [1]. 

Skogerboe, R.K. (1968), Optimization of Analytical Methods Using Designed Experiments, in Baer, W.K., Perkins, A.J., Grove, E.L., Eds, Developments in Applied Spectroscopy, 6, Plenum Press, New York, NY, pp. 127-142. 

N Naulet, P Tesson, N Couler, GJ Martin. Optimization of analytical methods for origin assessment of orange juices. II. Determination of the amino acid composition. Analusis 25 42-47, 1997. 

Lekkas,T.D.,M. Kostopoulou,G. Kolokythas,N.Thomaidis, S.Golfinopoulos,A. Kotrikla,G.Pavlogeorgatos, et al. 2003. Optimization of analytical methods for the determination of trace concentrations of toxic pollutants in drinking and surface waters. Global Nest J. 5 165-175. 

Models are constructed in analytics to describe the relationship between responses and factors. This is, for example, important for optimization of analytical methods on the basis of response surface methods (cf. Section 4.2). Models are also needed for cahbration of analytical methods. There, calibration of a single analyte in dependence on one or several wavelengths might be of interest. If, in the first example, the straight-line model would be adequate, for the second task of multiwavelength spectroscopy, multivariate approaches are needed. Calibrations in the case of unselective analytical methods must also be performed. These methods are termed simultaneous multicomponent analysis. In near-infrared (NIR) spectroscopy, the contents of water and protein in whole grain wheat are determined that way. 

As will be illustrated in Chapters 5 and 6, SIMS is able to effectively circumvent many of these complications through the optimization of analytical methods and post-processing procedures suited to the substrate and information of interest. Note Understanding the potential sources of error is, however, of prime importance in setting up such optimized analytical conditions, which in most cases are substrate and information content specific. 

Finally, the textbook concludes with two chapters discussing the design and maintenance of analytical methods, two topics of importance to analytical chemists. Chapter 14 considers the development of an analytical method, including its optimization, verification, and validation. Quality control and quality assessment are discussed in Chapter 15. 

D.L. Massart, A. Dijkstra and L. Kaufman, Evaluation and Optimization of Laboratory Methods and Analytical Procedures. Elsevier, Amsterdam, 1978. 

Advanced mathematical and statistical techniques used in analytical chemistry are often referred to under the umbrella term of chemometrics. This is a loose definition, and chemometrics are not readily distinguished from the more rudimentary techniques discussed in the earlier parts of this chapter, except in terms of sophistication. The techniques are applied to the development and assessment of analytical methods as well as to the assessment and interpretation of results. Once the province of the mathematician, the computational powers of the personal computer now make such techniques routinely accessible to analysts. Hence, although it would be inappropriate to consider the detail of the methods in a book at this level, it is nevertheless important to introduce some of the salient features to give an indication of their value. Two important applications in analytical chemistry are in method optimization and pattern recognition of results. 

A comprehensive two-volume Handbook of Chemometrics and Qualimetrics has been published by D. L. Massart et al. (1997) and B. G. M. Vandeginste et al. (1998) predecessors of this work and historically interesting are Chemometrics A Textbook (Massart et al. 1988), Evaluation and Optimization of Laboratory Methods and Analytical Procedures (Massart et al. 1978), and The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis (Massart and Kaufmann 1983). A classical reference is still Multivariate Calibration (Martens and Naes 1989). A dictionary with extensive explanations containing about 1700 entries is The Data Analysis Handbook (Frank and Todeschini 1994). 

Optimization of Laboratory Methods and Analytical Procedures" Elsevier, Amsterdam 1978. 

Morgan, S.L., and Deming, S.N. (1974), Simplex Optimization of Analytical Chemical Methods, Anal. Chem., 46, 1170-1181. 

Wolters, R., and Kateman, G. (1990), The Constmction of Simultaneous Optimal Experimental Designs for Several Polynomials in the Calibration of Analytical Methods, J. Chemometrics, 4, 171-185. 

Analytical change control is the monitoring of any changes to analytical methodology, which has to be justified and evaluated for its performance and impact on the quality of the product. For process analytical methods, the change control approach requires significant documentation and can impede the efficient optimization of the method in the early phases of implementation if parameters have to be locked down based on an insufficient data set. 

Finally, when RPC methods are used in preparative studies with peptides, the opportunity routinely exists for subsequent analysis of the recovered fractions by a variety of analytical methods including high-speed RP-HPLC, HP-IEX, HP-HILIC, or HP-IMAC, zonal or micellar electrokinetic high-performance capillary electrophoresis (HP-CZE and MECK-CZE), capillary electrochromatography (CEC), or capillary isotachophoresis. The combination of the RPC information, drawn from the In k versus i > plots, with the data derived from on-line spectroscopic detection thus readily provides a comprehensive opportunity to assess the purity of an isolated peptide, many of the physicochemical features of the interaction, as well as a means to optimize the resolution in the RPC separation. 

Already in 1955, Box mentioned that an evolutionary operation (EVOP) type method could be made automatic. Although the Simplex method has been critisized at many occasions, because it cannot handle situations with multiple optima or with excessive noise, its unique suitability for unattended and automatic optimization of analytical systems, explains the great effort by Chemometricians to make the method work. 

Oxine (5) fonns complexes of analytical applicability with various metal ions. A RP-HPLC-FLD method (Xex = 370 nm, Xg = 516 nm) was proposed for simultaneous determination of Al(III) and Mg(II), using a Cjg column. Various details of the method are noteworthy Optimization of the method showed that for both ions it is best to have also precolumn and in-column complex formation, caused by the presence of 5 in the injection loop and in the carrier solution FLD detection is preferable to simple UVD because it avoids the background of 5 and interference of various ions forming nonfluorescent chromogenic complexes, e.g. Ca(II) and Zn(II) the intensity of the fluorescence can be increased by micelle formation on addition of SDS and neutralized Af,Af-bis(2-hydroxyethyl)-2-aminoethanesulfonic acid (6). The LOD (SNR = 3) were 0.74 (xM (18 ppb) Mg(n) and 0.60 (xM (16 ppb) Al(III) the latter was attributed in part to residual impurities in the purified water -... 

Solid-phase organic syntheses typically use large excesses of reagents to drive reactions to completion so that, ideally, products liberated from resins should not require purification. Optimization of conditions is a critical part of solid-phase syntheses. Transfer of organic reactions in solution to a solid matrix is not a trivial undertaking, and lack of analytical methods accentuates this problem. Libraries prepared without adequate refinement of conditions tend to be of poor quality. For libraries so large that all the constituents cannot be fully characterized, well-optimized reaction conditions are absolutely essential. Techniques like split and pool, 2 for instance, can only be applied successfully after thorough optimization. 

A. Dijkstra, and L. Kaufman, Evaluation and Optimization of Laboratory Methods and Analytical Procedures (Amsterdam Elsevier, 1978) G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters An Introduction to Design Data Analysis and Model Building (New York Wiley, 1978) R. S. Strange, Introduction to Experimental Design for Chemists, J. Chem. Ed. 1990,67. 113. 

Chiralpak AD-RH column was studied by Kazusaki et al. [95], and the results are shown in Figure 21b. The chiral resolution of flubiprofen on Chiralpak AD-RH columns was studied by Aboul-Enein and Ah [92], They observed that the best resolution occurred at 25°C. Finally, the influence of temperature on enantios-electivity differs from one analyte to another. Thus researchers have an ideal tool for optimizing throughput per unit time in preparative chromatography or a possibility for improving the sensitivity of analytical methods. 

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