Chemometric analysis, chromatographic

P.J. Dunlop, C.M. Bignell, J.F. Jackson, D.B. Hibbert, Chemometric analysis of gas chromatographic data of oils from Eucalyptus species. Chemom. Intell. Lab. Systems 30 (1995) 59-67. K. Varmuza, F. Stangl, H. Lohninger and W. Werther, Automatic recognition of substance classes from data obtained by gas chromatography, mass spectrometry. Lab. Automation Inf. Manage., 31 (1996) 221-224. 

Turowski, M., Kaliszan, R., Lullmann, C., Genieser, H. G., and Jastorff, B., New stationary phases for the high performance liquid chromatographic separation of nucleosides and cyclic nucleotides. Synthesis and chemometric analysis of retention data, /. Chromatogr. A, 728, 201-211, 1996. 

Headspace analysis and SPME methods produce a wealth of chromatographic data and the best approach is to use chemometric analysis of selected chromatographic peaks under which circumstances identification of the individual compounds is not usually necessary. These techniques have been applied with some success to characterize olive oils (Morales and Aparicio, 1993 Morales etal., 1994). 

Johnson, K.J. Wright, B.W. Jarman, K.H. Synovec, R.E. (2003). High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. Journal of Chromatography A, Vol.996, No.1-2, (May 2003), pp. 141-155, ISSN 0021-9673... 

A. Cichelli and G.P. Pertesana, High-performance liquid chromatographic analysis of chlorophylls, pheophytins and carotenoids in virgin olive oils chemometric approach to variety classification. J. Chromatogr.A 1046 (2004) 141-146. 

In most applications chemometric methods are applied to analytical data in an off-line mode that is, data has already been obtained by conventional techniques and is then applied to a particular chemometric method. Examples of this use are in cluster analysis and in pattern recognition. They are applied to spectroscopic, chromatographic, and other analytical data. 

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. 

Chemists and statisticians use the term mixture in different ways. To a chemist, any combination of several substances is a mixture. In more formal statistical terms, however, a mixture involves a series of factors whose total is a constant sum this property is often called closure and will be discussed in completely different contexts in the area of scaling data prior to principal components analysis (Chapter 4, Section 4.3.6.5 and Chapter 6, Section 6.2.3.1). Hence in statistics (and chemometrics) a solvent system in HPLC or a blend of components in products such as paints, drugs or food is considered a mixture, as each component can be expressed as a proportion and the total adds up to 1 or 100%. The response could be a chromatographic separation, the taste of a foodstuff or physical properties of a manufactured material. Often the aim of experimentation is to find an optimum blend of components that tastes best, or provide die best chromatographic separation, or die material diat is most durable. 

The chemometric methods discussed above have found widespread applications in chromatography, and many theoretical and practical chromatographers have become familiar with these techniques and have applied them successfully. However, other less well-known methods have also found applicability in the analysis of chromatographic retention data. Thus, canonical variate analysis has been applied in pyrolysis GC-MS, artificial neural network for the prediction of GLC retention indices, and factor analysis for the study of the retention behavior of A-benzylideneaniline derivatives. 

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