Numerical Correction of Detector Channel Cross-Talk Using Full-Spectrum Fluorescence Correlation Spectroscopy

Abstract

Fluorescence correlation spectroscopy (FCS) uses fluctuations in the fluorescence collected from a small illuminated volume to measure dynamic processes of fluorophores. In traditional FCS, spectral overlap produces cross-talk in dedicated detector channels, undermining the accuracy of measurements of molecular interactions. Here, the experimental realization of full-spectrum fluorescence correlation spectroscopy is described and coupled with multivariate data analysis to numerically correct detector cross-talk, isolating spectra and fluctuation traces of mixture components in spite of overlap. Application of this methodology is illustrated using the measurement of the diffusion constant of labeled polystyrene in hydroxypropyl cellulose in the presence of a persistent dye. Additionally, the results show that full-spectrum FCS with multivariate analysis can isolate and characterize signals from unanticipated sample components.

Photoluminescence Probes in Data-Enabled Sensing

“…Figure 2 illustrates a selection of MPMs showing how spectral data expand as additional parameters are varied. A rigorous description of each of these combinations is not only beyond the scope of this review, but is unnecessary because most of these combinations have not been reported and most that have been described were not used to analyze probe emission (17,18,46). Moreover, as Table 1 illustrates, to describe an MPM, the expression for a single datum need only be expanded to reflect the variation in the relevant measurement parameters.…”

“…Warner and his colleagues were prescient, but not clairvoyant: Their article did not presage the coming explosion in hyperspectral (10)(11)(12) and lifetime imaging (13)(14)(15)(16), in spite of the fact that image detectors were critical components of the instruments they described. In the intervening years, advances in correlation spectroscopy (17) and flow cytometry (18) the photophysical and/or photochemical properties of complex samples is typically a multistep process that starts with exploratory data analysis, which may require signal processing and data visualization to ascertain or confirm the underlying structure of the data and the number of distinguishable components contributing to the data set. Depending on the measurement type and analysis objective(s), the data may be subjected to multivariate calibration to quantify analytes, pattern recognition to categorize signal components, mixture analysis to isolate analytes from concomitants, or some combination of these prototypical tasks.…”

Quantification of protein secondary structure content by multivariate analysis of deep-ultraviolet resonance Raman and circular dichroism spectroscopies

“…Therefore, the error in the estimate of the actual concentrations can be minimized by using eqn (7) on all predicted concentrations. [52][53][54][55] Occasionally, the predicted content for a particular secondary structure, helical for UVRR and b-sheet for CD, fell below zero. Given that the predicted amounts of secondary structure should be zero or greater, these values were set to zero.…”