Method optimization chemometric techniques

Multivariate chemometric techniques have subsequently broadened the arsenal of tools that can be applied in QSAR. These include, among others. Multivariate ANOVA [9], Simplex optimization (Section 26.2.2), cluster analysis (Chapter 30) and various factor analytic methods such as principal components analysis (Chapter 31), discriminant analysis (Section 33.2.2) and canonical correlation analysis (Section 35.3). An advantage of multivariate methods is that they can be applied in... 

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. 

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]. 

When from initial experiments, conditions that indicate the enantioselectivity of the system towards a given enantiomer pair or towards a limited series of substances are known, one might optimize their separation. To obtain optimal conditions, the different chemometric techniques used for method optimization in classic chromatographic or electrophoretic separations can also be applied for the chiral ones. Different experimental design approaches, using both screening and response surface designs can be In Reference 331, for... 

Advances in herbal medicines have hastened the need for high-throughput CE methods that can effectively screen and resolve numerous compounds in a short period of time. Chemometric experimental design and optimization techniques will continue to increase as new developments in sample preparation, method optimization, and data processing in (3E analysis of herbal medicines occur. 

The optimization of the variables is a critical step in the design of new analytical methods. Optimization involves the selection of the chemical and instrumental factors which may affect the analytical signal, and the choice of the values of the variables to obtain the best response from the chemical system. For this purpose, two different strategies can be used. In the traditional univariate optimization, all values of the different factors except one are constant, and this one is the object of the examination. The alternative to this strategy is the use of chemometric techniques based mainly on the use of experimental designs (Tarley, et al. 2009). 

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. 

Variable selection is an optimization problem. An optimization method that combines randomness with a strategy that is borrowed from biology is a technique using genetic algorithms—a so-called natural computation method (Massart et al. 1997). Actually, the basic structure of GAs is ideal for the purpose of selection (Davis 1991 Hibbert 1993 Leardi 2003), and various applications of GAs for variable selection in chemometrics have been reported (Broadhurst et al. 1997 Jouan-Rimbaud et al. 1995 Leardi 1994, 2001, 2007). Only a brief introduction to GAs is given here, and only from the point of view of variable selection. 

Massart, D.L., Dijkstra, A., Kaufman, L. Evaluation and Optimization of Laboratory Methods and Analytical Procedures. A Survey of Statistical and Mathematical Techniques, Elsevier, Amsterdam, 1978 Morgan, E. Chemometrics Experimental Design, Wiley, Chichester, 1991... 

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. 

CE also suffers from several weaknesses as an analytical technique (e.g., adsorption of charged species to the capillary wall, presence of Joule heating). Hence, it is important to be able to determine optimal conditions in CE method development (23). Various chemometric-based techniques including multivariate experimental design and response surface methodology have been devised to help optimize the performance of a system (23-26). 

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