Moving Window Two-Dimensional Correlation Spectroscopy and Determination of Signal-To-Noise Threshold in Correlation Spectra

In this paper we report two new developments in two-dimensional (2D) correlation spectroscopy; one is the combination of the moving window concept with 2D spectroscopy to facilitate the analysis of complex data sets, and the other is the definition of the noise level in synchronous/asynchronous maps. A graphical criterion for the latter is also proposed. The combination of the moving window concept with correlation spectra allows one to split a large data matrix into smaller and simpler subsets and to analyze them instead of computing overall correlation. A three-component system that mimics a consecutive chemical reaction is used as a model for the illustration of the two ideas. Both types of correlation matrices, variable–variable and sample–sample, are analyzed, and a very good agreement between the two is met. The proposed innovations enable one to comprehend the complexity of the data to be analyzed by 2D spectroscopy and thus to avoid the risks of over-interpretation, liable to occur whenever improper caution about the number of coexisting species in the system is taken.

[1]  Window evolving factor analysis for assessment of peak homogeneity in liquid chromatography , 1993 .

[2]  M. Maeder.  Evolving factor analysis for the resolution of overlapping chromatographic peaks , 1987 .

[3]  Yukihiro Ozaki,et al.  New Insight into the Mathematical Background of Generalized Two-Dimensional Correlation Spectroscopy and the Influence of Mean Normalization Pretreatment on Two-Dimensional Correlation Spectra , 2001 .

[4]  Y. Ozaki,et al.  Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment. , 2001, Analytical chemistry.

[5]  S. Engelsen,et al.  Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy , 2000 .

[6]  T. Isaksson,et al.  Synchronicity and Linearity in Generalized Two-Dimensional Correlation Spectroscopy: Concepts Relevant to the Analysis of Nonperiodic, Monotonically Increasing or Decreasing Spectroscopic Signals , 2002 .

[7]  I. Noda,et al.  Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform , 2000 .

[8]  I. Noda.  Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy , 1993 .

[9]  C. Visy,et al.  Evolutionary spectral factor analysis of doping-undoping processes of thin conductive polymer films , 1990 .

[10]  Yukihiro Ozaki,et al.  A New Possibility of the Generalized Two-Dimensional Correlation Spectroscopy. 2. Sample−Sample and Wavenumber−Wavenumber Correlations of Temperature-Dependent Near-Infrared Spectra of Oleic Acid in the Pure Liquid State , 2000 .

[11]  S. Wold,et al.  Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. , 2002, Analytical chemistry.

[12]  A novel tool for two-dimensional correlation spectroscopy , 1995 .

[13]  Mike Thomas,et al.  Two-dimensional FT-IR correlation analysis of the phase transitions in a liquid crystal, 4′-n-octyl-4-cyanobiphenyl (8CB) , 2000 .

[14]  Peter de B. Harrington,et al.  Two-dimensional correlation analysis , 2000 .

[15]  Yukihiro Ozaki,et al.  Potentials of variable–variable and sample–sample, generalized and statistical, two-dimensional correlation spectroscopies in investigations of chemical reactions , 2003 .

[16]  H. R. Keller,et al.  Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis , 1991 .

[17]  M M Darj,et al.  Complexation between Copper(II) and Glycine in Aqueous Acid Solutions by Window Factor Analysis of Visible Spectra. , 1996, Analytical chemistry.

[18]  W. Windig.  Spectral data files for self-modeling curve resolution with examples using the Simplisma approach , 1997 .

[19]  Chris W. Brown,et al.  Self-Modeling Mixture Analysis by Interactive Principal Component Analysis , 2000 .

[20]  Edmund R. Malinowski,et al.  Window factor analysis: Theoretical derivation and application to flow injection analysis data , 1992 .

[21]  Yukihiro Ozaki,et al.  A New Possibility of the Generalized Two-Dimensional Correlation Spectroscopy. 1. Sample-Sample Correlation Spectroscopy , 2000 .

[22]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[23]  P. Harrington,et al.  Effects of static spectrum removal and noise on 2D-correlation spectra of kinetic data , 1998 .

[24]  Yukihiro Ozaki,et al.  Wavelength—Wavelength and Sample—Sample Two-Dimensional Correlation Analyses of Short-Wave Near-Infrared Spectra of Raw Milk , 2001 .