Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

Chi-Ying Chang,Chia-Chi Chang,Tzu-Chien Hsiao

doi:10.3390/ijms141122436

Abstract

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 non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes.

Highlights

Fluorescence spectroscopy plays an important role in the clinical detection of cancer tissue
The aim of this study was to examine the ability of multi-dimensional ensemble empirical mode decomposition (MEEMD) to extract intrinsic features from
The features in the Excitation-emission matrix (EEM), which are related to chemical compounds, were complicated, and some of the features had small variances

Summary

Introduction

Fluorescence spectroscopy plays an important role in the clinical detection of cancer tissue. Some of the chemical compounds, such as nicotinamide adenine dinucleotide (NADH), tryptophan, and collagen, are related to the changes in its progression. These compounds can be detected by their fluorescence properties [1]. Fluorescence spectroscopy has been used as a non-invasive method for the detection of lesion tissue. Traditional studies have used the difference in the fluorescence spectra between normal tissue and lesion tissue for disease discrimination, such as oral cancer [2] and cervical cancer [3]. Some studies determined specific spectral features that are associated with certain compounds in normal and lesion tissue, such as NADH and collagen [2,4,5]. To improve the feasibility and accuracy of fluorescence spectroscopy analysis, advanced feature-extraction methods, such as partial least-squares (PLS)

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Conclusion