Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/59365
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Chemijos inžinerija / Chemical engineering (T005);Informatika / Computer science (N009)
Author(s): Gorbatšova, Jelena;Drevinskas, Tomas;Telksnys, Adolfas Laimutis;Maruška, Audrius;Kaljurand, Mihkel
Title: Capillary electrophoresis sensitivity enhancement based on adaptive moving average method
Is part of: Analytical chemistry. Washington : American Chemical Society, 2018, Vol. 90, iss. 11
Extent: p. 6773-6780
Date: 2018
Note: This research was funded by a grant (No. 09.3.3-LMT-K-712-02- 0202) from the Research Council of Lithuania
Keywords: Capillary electrophoresis;Electrophoresis signal analysis;Average algorithm
Abstract: In the present work, we demonstrate a novel approach to improve the sensitivity of the “out of lab” portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in fieldportable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods
Internet: https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b00664
https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b00664
Affiliation(s): Gamtos mokslų fakultetas
Informatikos fakultetas
Instrumentinės analizės atviros prieigos centras
Sistemų analizės katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml11.87 kBXMLView/Open

MARC21 XML metadata

Show full item record

Page view(s)

152
checked on Nov 2, 2019

Download(s)

10
checked on Nov 2, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.