Paper
15 October 2007 Application of spectral unmixing in multi-wavelength time-resolved spectroscopy
D. Chorvat Jr., A. Mateasik, J. Kirchnerova, A. Chorvatova
Author Affiliations +
Proceedings Volume 6771, Advanced Photon Counting Techniques II; 677105 (2007) https://doi.org/10.1117/12.732880
Event: Optics East, 2007, Boston, MA, United States
Abstract
We present a new approach for analysis of multi-wavelength time-resolved spectroscopy data, based on sequential spectral unmixing. Principal component analysis was used to identify the number and spectral profiles of the main components of intrinsic flavin signal in multi-wavelength time-resolved fluorescence recordings from isolated living cardiac myocytes. To determine these components, natural variations in the cardiomyocyte autofluorescence spectra were induced by modulators of mitochondrial metabolism and respiration. Using aforementioned approach we have identified two main components of intrinsic flavin emission in cardiac myocytes. The first component show emission maximum at 486-504 nm and mean lifetime of 1.2 nanoseconds, the second component with peak at 522 nm has two-exponential decay with fluorescence lifetimes of 0.3 and 3.1 nanoseconds. Comparison of gathered new results to our previous studies of flavins in vitro and in cardiac cells clearly points to the fact that the estimated spectral components correspond to flavin adenine dinucleotide (FAD) bound to enzyme(s) of mitochondrial metabolic chain, and to free FAD, respectively.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
D. Chorvat Jr., A. Mateasik, J. Kirchnerova, and A. Chorvatova "Application of spectral unmixing in multi-wavelength time-resolved spectroscopy", Proc. SPIE 6771, Advanced Photon Counting Techniques II, 677105 (15 October 2007); https://doi.org/10.1117/12.732880
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Cited by 8 scholarly publications.
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KEYWORDS
Luminescence

Data modeling

Algorithm development

Time resolved spectroscopy

Computer simulations

Statistical analysis

Principal component analysis

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