Download (direct link):
7.3.2 MODEL-BASED 2D CORRELATION SPECTROSCOPY
Elmore and Dluhy introduced a new and intriguing form of 2D correlation analysis called fiv correlation,43 based on the correlation between the spectral data
and a set of kernel functions with varying phase shift. The kernel is a single sinusoid corresponding to the lowest Fourier component of the signal, such that one quarter cycle of the sinusoid is set to be the entire observation period. This is probably the first report on a 2D correlation analysis based purely on a model function. 2D IR Pv correlation has been applied to IRRAS data of a binary phospholipid mixture at an interface.44 2D IR Pv correlation analysis of a pulmonary surfactant at the air-water interface with surface pressure effect has also been reported.45
Eads and Noda formally put forward the concept of the model-based 2D correlation analysis.46 The technique is sometimes referred to as the 2D waveform correlation analysis. Their method may be regarded as a form of heterocorrelation analysis, where one of the data sets is a collection of model calculations with systematically varying adjustable parameters. The highest correlation is achieved when the model parameter matches closest to the function resembling the actual data. The resulting 2D spectrum then becomes an intuitively understandable visual representation of the least squares curve fitting of the model function to experimental measurements. The technique is robust and generally applicable to a wide ranges of studies.
2D waveform correlation analysis was applied by Eads and Noda to a set of NMR spectra obtained during the diffusion of multiple components in a solution mixture. The Fickian diffusion of small molecules is known to lead to Gaussian-like relaxation profiles of NMR signals. When the diffusivity of component molecules is used as an adjustable parameter, the 2D waveform correlation analysis based on a set of model diffusion profiles immediately yielded individual diffusivities of the component species, even in a complex mixture.
Model-based 2D correlation is a very promising development, which can provide a major departure from the current limitation of the scope of correlation analysis. Unlike the strictly phenomenological data treatment of conventional 2D correlation analysis, model-based 2D correlation can bring in the idea of hypothesis testing and even the determination of causal relationship to the data analysis, at least within the boundary of the proposed model structure. In the past, 2D correlation analysis provided an unbiased model-free procedure to directly extract pertinent information from a spectral data set. This time, however, the user of 2D correlation spectroscopy must think of and propose a hypothesis before carrying out the data analysis. It could be an advantage or disadvantage, depending on the level of prior information we have on the system to be studied.
1. Y. Tanimura and S. Mukamel, J. Chem. Phys., 99, 9496 (1993).
2. K. Tominaga and K. Yoshihara, Phys. Rev. Lett., 74, 3061 (1995).
3. T. Steffen and K. Duppen, Phys. Rev. Lett., 76, 1224 (1996).
4. A. Tokmakoff and G. R. Fleming, J. Chem. Phys., 106, 2569 (1997).
Other Types of Two-dimensional Spectroscopy
5. A. Tokmakoff, M. J. Lang, D. S. Larsen, G. R. Fleming, V. Chernyak, and S. Mu-kamel, Phys. Rev. Lett., 79, 2702 (1997).
6. W. Zhao and J. C. Wright, Phys. Rev. Lett., 83, 1950 (1999).
7. M. T. Zanni, N. H. Ge, Y. S. Kim, and R. M. Hochstrasser, Proc. Natl. Acad. Sci. USA, 98, 11265 (2001).
8. R. R. Ernst, G. Bodenhausen, and A. Wakaun, Principles of Nuclear Magnetic Resonance in One and Two Dimensions, Oxford University Press, Oxford, 1987.
9. M. Cho, Adv. Multi-Photon Processes Spectrosc. 12, 229 (1999).
10. H. Hamaguchi, Acta Phys. Pol., A95, 37 (1999).
11. S. Mukamel, A. Piryatinski, and V. Chernyak, Acc. Chem. Res., 32, 145 (1999).
12. K. Tominaga and H. Maekawa, J. Lumin., 87/89, 101 (2000).
13. M. Cho, Phys. Chem. Comm., 7, 1 (2002).
14. S. Woustersen and P. Hamm, J. Phys. Condens. Mater., 14, R1035 (2002).
15. I. Noda, Bull. Am. Phys. Soc., 31, 520 (1986).
16. I. Noda, J. Am. Chem. Soc., 111, 8116 (1989).
17. I. Noda, Appl. Spectrosc., 44, 550 (1990).
18. C. Marcott, A. E. Dowrey, and I. Noda, Anal. Chem., 66, 1065A (1994).
19. L. J. Frasinski, K. Codling, P. A. Hatherly, Science, 246, 1029 (1989).
20. C. Marcott, I. Noda, and A. E. Dowrey, Analytica Chim. Acta, 250, 131 (1991).
21. F. E. Barton II, D. S. Himmelsbach, J. H. Duckworth, and M. J. Smith, Appl. Spectrosc., 46, 420 (1992).
22. F. E. Barton II, D. S. Himmelsbach, A. M. McClung, and E. L. Champagne, Cereal Chem., 79, 143 (2002).
23. S. Sasic and Y. Ozaki, Anal. Chem., 73, 2294 (2001).
24. S. Sasic, J.-H. Jiang, and Y. Ozaki, Chemom. Intel. Lab. Syst., 65, 1 (2003).
25. A. S. Barros, M. Safar, M. F. Devaux, P. Robert, D. Bertland, and D. N. Rutledge, Appl. Spectrosc., 51, 1384 (1997).
26. W. Windig, D. E. Margevich, and W. P. McKenna, Chemom. Intel. Lab. Syst., 28, 109 (1995).