Intrinsic fluorescence feature extraction of excitation-emission matrix by using multi-dimensional ensemble empirical mode decomposition
白、為生素 D 等。整體而言，多維整體經驗模態分析法提供一個光譜分析的新觀
Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method decomposes data by subtracting local means iteratively and can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to extract the intrinsic characteristics of EEM by using MEEMD. We use simulated signal to examine the decomposition ability of MEEMD on decomposing signal which was similar to EEM but simpler than EEM, and then MEEMD was applied to decompose EEM. PCA was used to compare with MEEMD in this study. The results indicate that although PCA provides the main spectral features associated with chemical compounds, which mainly contributed by collagen, MEEMD can provide additional intrinsic features with more reliable mapping of individual chemical compounds, e.g. collagen and vitamin D. Overall, MEEMD provide a new point of view on EEM analysis and has the potential to extract intrinsic fluorescence features and improve the detection of biological fluorophores.of individual chemical compounds, e.g. collagen and vitamin D. Overall, MEEMD provide a new point of view on EEM analysis and has the potential to extract intrinsic fluorescence features and improve the detection of biological fluorophores.
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