Methods And Application Of Differentiation Of Two-dimensional Correlation Infrared Spectroscopy

Analysis of the complex mixture has always been the hotspot in the relevant research fields. Macro-fingerprints of multi-level infrared spectroscopy have been used for the global analysis of mixtures more and more today. Tri-level infrared spectroscopic identification, which employs the Fourier transform infrared spectroscopy (FT-IR), the second derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2D-IR) to discriminate quite similar mixtures, has proved to be useful. The systematic methods to analysis the differences between 2D-IR spectra were developed by this research, based on chemometrics and both simulated and practical data.The basic theory, experimental details, the preprocessing and normalization of the original spectra and the selection of regions for discriminating mixture samples such as traditional Chinese medicine (TCM) and food was summarized at first. The distance and correlation coefficient between 2D spectra, as well as the symmetry distance and symmetry coefficient of a hetero 2D spectrum, were introduced for the first time to evaluate the differences between 2D spectra quantitatively.The differential and derivative 2DIR were also discussed for the first time by simulated and experimental data. Strong but useless peaks for the identification of samples would fade down on differential 2D spectra while the minor peaks varied much between different samples could be magnified. Overlapped peaks on original 2D spectra could be separated by the derivative processing and more information would come out. Both the differential and derivative 2DIR could make the differences between quite similar samples become much more significant.The statistic and principal components analysis (PCA) of the 2DIR spectra were used for the first time to differentiate plenty of samples. The common features of the samples could be found by the mean 2D spectrum. The spectral regions where different samples could be discriminated effectively could be shown on the standard deviation, skewness and kurtosis of the 2D spectra. Samples could be clustered accurately by the scores plots and the most important spectral regions could be found by the loadings plots of the PCA analysis of the 2DIR spectra.Methods for differentiating similar 2D spectra were well set up by this research. Guidance and techniques for the identification of mixtures such like the traditional Chinese medicine (TCM) and the food by 2DIR were provided.