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Correction for contaminant fluorescence in frequency-domain fluorometry.

J R Lakowicz, R Jayaweera, N Joshi

Analytical Biochemistry

|February 1, 1987

Summary

This study presents a general method to correct contaminant fluorescence in frequency-domain fluorometry. The technique accurately removes background signals from scattered light or impurities, improving data reliability.

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ADVANCES IN FLUORESCENCE SPECTROSCOPY: MULTI-PHOTON EXCITATION, ENGINEERED PROTEINS, MODULATION SENSING AND MICROSECOND RHENIUM METAL-LIGAND COMPLEXES.

Acta physica Polonica: A·2019

Area of Science:

Analytical Chemistry
Spectroscopy

Background:

Frequency-domain fluorometry is a powerful technique for analyzing fluorescent samples.
Contaminant fluorescence and scattered light can interfere with accurate measurements.
Existing methods may not universally address diverse background signal origins.

Purpose of the Study:

To develop a general method for correcting contaminant fluorescence in frequency-domain fluorometry.
To provide a robust approach for handling background signals from various sources.
To ensure accurate data analysis by properly accounting for uncertainties.

Main Methods:

Measuring frequency-dependent phase and modulation of the background signal.
Approximating the background decay law using multiple frequency measurements.

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Propagating measurement uncertainties into corrected phase and modulation values.

Utilizing least-squares fitting algorithms with proper data weighting.

Main Results:

A general method for correcting contaminant fluorescence was successfully developed.
The method effectively removes background signals from scattered light and impurity fluorescence.
Uncertainty propagation ensures accurate weighting of frequency-dependent data.
Demonstrated utility with one- and two-component mixtures contaminated with background signals.

Conclusions:

The described method offers a versatile solution for background correction in frequency-domain fluorometry.
Accurate correction of contaminant fluorescence enhances the reliability of analytical results.
This approach is crucial for precise quantitative analysis in complex samples.