Optimization of Analytical Procedures

Analytical procedures should always run under optimum conditions. That means that for Eq. (5.6), which is here used only with two factors, the coefficients have to be chosen in such a way that y becomes an optimum 

In analytical chemistry the target quantity y which has to be optimized is frequently the signal intensity, absolute or relative (signal-to-noise ratio), but occasionally other parameters like yields of extractions or chemical reactions, too. The classical way to optimize influences, e.g., in an optimization space as shown in Fig. 5.3a is to study the factors independently one after the other. In Fig. 5.3b,c it can be seen that an individual optimum will be found in this way. 

However, the optima of X and x2 found in this way do not meet the global optimum of the response surface which is situated at x = 80 and x2 = 150. Because the global optimum is rarely found by such an obsolete proceeding, multivariate techniques of optimization should be applied. 

The most reliable technique to find the global optimum by means of common methods is the transition from the quasi-two-dimensional approach (Fig. 5.3b,c) to a complete two-dimensional one. It consists of a certain number of experiments as shown in Fig. 5.4. 

On the basis of the grid experiments a mathematical function y = f(xi, 2, 3, ), called the response surface, is estimated that characterizes the response as a function of the factors. In case of only two factors the response surface can be visualized by plots like that in Fig. 5.5. 

---

Wienke D, Lucasius C, Kateman G (1992) Multicriteria target vector optimization of analytical procedures using a genetic algorithm. Part I. Theory, numerical simulations and application to atomic emission spectroscopy. Anal Chim Acta 265 211... 

Analytical Chemistry the group of Prof Vasil D. Simeonov is performing research in analytical chemistry, chemometrics, environmetrics, multivariate calibration classification, interpretation and modelling of environmental data sets evaluation and optimization of analytical procedures potentiometry with ion selective electrodes atmospheric and marine chemistry. A very wide network of international collaboration is associated with the group. 

D. Wienke, C. B. Lucasius, and G. Kateman, Anal. Chim. Acta, 265, 211 (1992). Multicriteria Target Vector Optimization of Analytical Procedures Using a Genetic Algorithm. 

Optimization of analytical procedure for analysis of samples with widely ranging concentration of ions... 

Vander Heyden, Y., Khots, M. S., and Massart, D. L., Three-Level Screening Designs for the Optimization or the Ruggedness Testing of Analytical Procedures, Analytica Chimica Acta 276, 1993, 189-195. 

The concept of SPME was first introduced by Belardi and Pawliszyn in 1989. A fiber (usually fused silica) which has been coated on the outside with a suitable polymer sorbent (e.g., polydimethylsiloxane) is dipped into the headspace above the sample or directly into the liquid sample. The pesticides are partitioned from the sample into the sorbent and an equilibrium between the gas or liquid and the sorbent is established. The analytes are thermally desorbed in a GC injector or liquid desorbed in a liquid chromatography (LC) injector. The autosampler has to be specially modified for SPME but otherwise the technique is simple to use, rapid, inexpensive and solvent free. Optimization of the procedure will involve the correct choice of phase, extraction time, ionic strength of the extraction step, temperature and the time and temperature of the desorption step. According to the chemical characteristics of the pesticides determined, the extraction efficiency is often influenced by the sample matrix and pH. 

Multifactorial experiments are used in analytical chemistry for diverse applications, e.g., checking up significant influences before optimization procedures, recognizing matrix effects, and testing the robustness of analytical procedures (Wegscheider [1996]). 

Optimization and Calibration of Analytical Procedure. The first step undertaken in the laboratory was the establishment of an optimum procedure for determining HCCP, HCBD, and 1,2-DCP by GC. (Other analytical techniques were eliminated on the basis of a preliminary literature search.) Two tasks were involved ... 

Table II. Optimized GC Analytical Procedure for Each of the Chlorocarbonsa...

Apart from establishing analytical validation parameters, other activities should include experimental optimization of each procedural step or method manipulation to determine the critical control steps that have a substantial impact on method performance. The ruggedness or process variability that may be employed in any particular method step, without reducing method performance, should be determined. It should be identified, for example, whether an analytical method may be stopped without adversely affecting the result. 

An excellent review on experimental design which is followed by a valuable discussion of numerous scientific and educational aspects is given by STEINBERG and HUNTER [1984], In analytical chemistry experimental design has been used to optimize almost all types of analytical procedure because of its ease of use. 

CONTENTS 1. Chemometrics and the Analytical Process. 2. Precision and Accuracy. 3. Evaluation of Precision and Accuracy. Comparison of Two Procedures. 4. Evaluation of Sources of Variation in Data. Analysis of Variance. 5. Calibration. 6. Reliability and Drift. 7. Sensitivity and Limit of Detection. 8. Selectivity and Specificity. 9. Information. 10. Costs. 11. The Time Constant. 12. Signals and Data. 13. Regression Methods. 14. Correlation Methods. 15. Signal Processing. 16. Response Surfaces and Models. 17. Exploration of Response Surfaces. 18. Optimization of Analytical Chemical Methods. 19. Optimization of Chromatographic Methods. 20. The Multivariate Approach. 21. Principal Components and Factor Analysis. 22. Clustering Techniques. 23. Supervised Pattern Recognition. 24. Decisions in the Analytical Laboratory. 

The display of signal versus time curves in real time is very important for the development of analytical procedures. In atomic absorption spectrometry with electrothermal atomization this is now indispensable and is an integral part of the development of an analytical procedure to be applied for a given analytical task. It is of further importance during the optimization of the plasma working parameters in ICP-AES and is certainly very useful for the optimization of the spectrometer with respect to drift and as a result of changes in any of the working parameters. 

Some features of analytical procedures developed for MLC are explained below, including sample preparation, derivatization of the drugs and optimization of the chromatographic separation. 

Figure 1.2 Summary of the several steps that must be integrated in an overall optimized quantitative analytical procedure. (Note that a mass spectrometer, although a key component, appears only in the Instrumental Analysis steps (and to some extent in the purity analysis of the calibration standard)).

This analytical procedure represents a typical example of how to carry out Hgx determinations in seawater. Because of many procedural and equipment-related variables, users are urged always to optimize the procedure with respect to the prevailing conditions. 11 for instance, the volume of seawater used for the analysis (200mL) has to be increased due to insufficient sensitivity of the available detector, it is necessary not only to change the volumes of applied reagents appropriately but also to optimize the analytical procedure under the newly established conditions. 

In this chapter, six issues are addressed with a view to providing guidelines for the optimization of calibration procedures, as well as of routine analytical methods in general ... 

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. 

---