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B) Scores plot generated from PCA of the selected features obtained with the f-ratio threshold set to 150. (Adapted and modiﬁed from Ref. ) 32 / Synovec et al. The feature selected 2-D data was considerably more adept at providing a useful classiﬁcation according to fuel type. Thus, the combination of GC ϫ GC with ANOVA-based feature selection was found to be a useful tool to enhance the chemical selectivity, and thus the classiﬁcation power, of the analytical procedure. It is likely that this analytical approach of “smart” pattern recognition may have applications to data from other separation methods and research areas as well, such as with LC ϫ CE data acquired in proteomics research [102–104].
B. Objective Run-to-Run Retention Time Alignment Although there have been many applications to chromatographic data, the bulk of chemometric research to date has centered on the development and application of chemometric algorithms toward spectroscopic rather than chromatographic data. This is because chromatographic data exhibit time axis uncertainty due to unavoidable variations in instrument performance between and during separations. The uncertainty, or limited precision, is observed as shifting in the retention times of chromatographic peaks between chromatograms.
E. Synovec, Anal. , 67: 631 (1995). R. B. Poe and S. C. Rutan, Anal. Chim. Acta, 283: 845 (1993). R. Andersson and M. D. Ha¨ma¨la¨inen, Chemom. Intell. Lab. , 22: 49 (1994). B. Grung and O. M. Kvalheim, Anal. Chim. Acta, 304: 57 (1995). N. P. V. Nielsen, J. M. Carstensen, and J. Smedsgaard, J. Chromatogr. A, 805: 17 (1998). R. Bro, C. Andersson, and H. Kiers, J. , 13: 295 (1999). C. G. Fraga, B. J. Prazen, and R. E. Synovec, 24th International Symposium on Capillary Chromatography and Electrophoresis, Las Vegas, NV, USA, May 22, 2001, Abstract 64.