[Extraction of characteristic parameters of three-dimensional fluorescence spectra of tyrosine and tryptophan].

In the present paper, three-dimensional fluorescence spectra of tyrosine and tryptophan were detected through Pekin-Elmer LS55 fluorescence spectrometer made in U. S. A, with the excitation wavelength in the range of 230-320 nm, interval of 5 nm, emission wavelength in the range of 230-500 nm, and interval of 2 nm. Three-dimensional fluorogram was obtained using excitation wavelength, emission wavelength and fluorescence intensity detected by Pekin-Elmer LS55 as threedimensional system of coordinate. It can be seen that the main peak of the three-dimensional fluorescence spectra of tyrosine and tryptophan was aliasing evidently, so it was impossible to distinguish these two overlapping components using excitation wavelength, emission wavelength and fluorescence intensity easily. Therefore, to distinguish spectra of tyrosine and tryptophan obviously, characteristic parameter was extracted based on the principle of mathematical statistics and the most relevant information of the fluorescence spectra of tyrosine and tryptophan was obtained. The results showed that the difference value percentage of the "mean", "deviation", "origin pitch" and "mixed center pitch" of the three-dimensional spectra' s characteristic parameter was 330.37%, 102.86%, 329.16% and 329.63%, respectively; meanwhile, the difference value percentage of "distribution" and "correlation" was 10.61% and 2.40% respectively. Thus, it was obvious that the "mean", "deviation", "origin pitch" and "mixed center pitch" of the three-dimensional spectra's characteristic parameter can be used to distinguish overlap spectra of tyrosine and tryptophan as sensitive characteristic parameters. The principle and results of this method are applicable and valid. This "math pre-extracted" three-dimensional spectrometry found sensitive parameter among the components by the parameter extracting and can replace traditional excitation-emission-intensity three-dimensional fluorescence analysis, then can be generalized to the identification of multi-components.