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Thermal marks as a signal processing aid for a portable capillary electropherograph.

Andrus Seiman¹, Merike Vaher, Mihkel Kaljurand

¹Department of Chemistry, Tallinn University of Technology, Akadeemia tee 15, Tallinn, Estonia. andrusseiman@gmail.com

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|March 31, 2011

Summary

Signal processing algorithms enhance capillary electrophoresis (CE) data analysis for field experiments. These tools improve nerve agent detection by correcting baseline fluctuations and aligning electropherograms for accurate peak identification.

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Area of Science:

Analytical Chemistry
Separation Science
Chemical Sensing

Background:

Interpreting raw capillary electrophoresis (CE) signals can be difficult due to unknown peaks and signal corruption from factors like baseline fluctuations and electroosmotic flow (EOF) velocity drift.
Effective data analysis is crucial for reliable results, especially in field applications.
Portable CE instruments require robust signal processing for accurate detection, such as for nerve agents.

Purpose of the Study:

To develop and present a suite of signal processing algorithms for capillary electrophoresis (CE) data analysis.
To enable accurate interpretation of CE data obtained from field experiments, particularly for nerve agent detection.
To address challenges in raw signal interpretation, including baseline drift and migration time irreproducibility.

Main Methods:

Developed algorithms for baseline correction using interpolation based on local extremes and outlier testing.
Implemented EOF drift compensation using thermal marks to correct irreproducible migration times.
Utilized a fuzzy matching algorithm for peak identification by comparing sample electropherogram peaks to a reference electropherogram.

Main Results:

The developed algorithms provide comprehensive signal processing capabilities for CE data.
Methods effectively correct baseline fluctuations and electroosmotic flow drift, improving data reliability.
Peak matching and identification are enhanced through fuzzy matching against reference data.

Conclusions:

The developed signal processing algorithms are essential for interpreting challenging CE data in field settings.
These tools significantly improve the accuracy and reliability of nerve agent detection using portable CE instruments.
The suite offers a complete solution from baseline correction to peak identification for robust CE analysis.