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Rapid Fluorescence EEM Spectroscopy Using Super-Cycle Hadamard-Transform Multiplexing.

Travis Ferguson1, Hans-Peter Loock2

  • 1Department of Chemistry, Queen's University, Kingston K7L3N6, Ontario, Canada.

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|August 15, 2023
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Summary
This summary is machine-generated.

Hadamard-transform (HT) multiplexing significantly accelerates fluorescence excitation emission matrix (EEM) spectroscopy. New data processing methods further boost acquisition rates by an order of magnitude, enabling rapid chemical analysis.

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Area of Science:

  • Spectroscopy
  • Analytical Chemistry
  • Multivariate Data Analysis

Background:

  • Hadamard-transform (HT) multiplexing reduces data acquisition time in fluorescence excitation emission matrix (EEM) spectroscopy.
  • Previous methods were limited by the time to record a full EEM cycle.
  • Chemical information extraction relies on parallel factor (PARAFAC) analysis of EEM sequences.

Purpose of the Study:

  • To increase the data acquisition rate for HT multiplexed EEM spectroscopy by another order of magnitude.
  • To develop an improved data processing algorithm applicable to existing HT multiplexed data.
  • To enable faster chemical information extraction from spectroscopic data.

Main Methods:

  • Developed a novel algorithm based on improved data processing of raw, multiplexed data.
  • Applied PARAFAC multivariate analysis directly to the raw (multiplexed) data set.
  • Obtained time-independent PARAFAC loading vectors before time-dependent score vectors, using a rolling-average approach for stability.

Main Results:

  • Achieved a data acquisition rate limited by single excitation spectrum recording time.
  • Demonstrated the algorithm's applicability to previously recorded HT multiplexed data sets.
  • Obtained fluorescence EEM scores with seven excitation and over 1000 emission wavelengths in under 20 ms.

Conclusions:

  • The new data processing algorithm significantly enhances the speed of HT multiplexed EEM spectroscopy.
  • This advancement allows for rapid acquisition of chemical information from complex spectroscopic data.
  • The method is robust and applicable to existing datasets, accelerating chemical analysis workflows.