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Published on: August 19, 2021
A similarity measure for two-dimensional fluorescent spectra
Wenliang Bai1, Ming Ren, Philip K Hopke
1School of Chemistry and Chemical Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China.
Applied Spectroscopy
|July 11, 2009
Summary
A novel similarity measure quantifies differences in two-dimensional fluorescent spectra using matrix analysis and hypothesis testing. This method effectively distinguishes spectral variations, as demonstrated with red wine samples.
Area of Science:
- Analytical Chemistry
- Spectroscopy
Background:
- Two-dimensional fluorescence spectroscopy is crucial for analyzing complex samples.
- Quantifying spectral similarities and differences requires robust statistical methods.
Purpose of the Study:
- To develop a quantitative similarity measure for differentiating two-dimensional fluorescent spectra.
- To establish a hypothesis testing framework for spectral comparison.
Main Methods:
- Digitalizing spectra into matrices.
- Calculating a difference matrix and its scalar mean.
- Employing Bayesian prior and posterior odds ratios for similarity quantification.
Main Results:
- The developed method successfully differentiates between two-dimensional fluorescent spectra.
- Hypothesis testing based on the difference matrix effectively identifies spectral variations.
- Application to Changyu red wine spectra demonstrated the method's efficacy.
Conclusions:
- A robust similarity measure for two-dimensional fluorescent spectra has been established.
- The method provides a quantitative approach to spectral differentiation.
- This technique holds potential for applications in sample analysis and quality control.
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