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Article

Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

1
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
2
Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 12/14, 61-704 Poznań, Poland
3
Institute of Plant Genetics, Polish Academy of Sciences, Strzeszyńska 34, 60-479 Poznań, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Silas G. Villas-Boas
Metabolites 2021, 11(4), 214; https://doi.org/10.3390/metabo11040214
Received: 2 March 2021 / Revised: 25 March 2021 / Accepted: 29 March 2021 / Published: 31 March 2021
(This article belongs to the Section Bioinformatics and Data Analysis)
Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms. View Full-Text
Keywords: chromatographic peak separation; chemometrics of chromatographic data; computational peak deconvolution; functional principal component analysis; simulation; metabolomics chromatographic peak separation; chemometrics of chromatographic data; computational peak deconvolution; functional principal component analysis; simulation; metabolomics
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MDPI and ACS Style

Sawikowska, A.; Piasecka, A.; Kachlicki, P.; Krajewski, P. Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis. Metabolites 2021, 11, 214. https://doi.org/10.3390/metabo11040214

AMA Style

Sawikowska A, Piasecka A, Kachlicki P, Krajewski P. Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis. Metabolites. 2021; 11(4):214. https://doi.org/10.3390/metabo11040214

Chicago/Turabian Style

Sawikowska, Aneta, Anna Piasecka, Piotr Kachlicki, and Paweł Krajewski. 2021. "Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis" Metabolites 11, no. 4: 214. https://doi.org/10.3390/metabo11040214

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