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Title: Quantitative structure-retention relationships with non-linear programming for prediction of chromatographic elution order
Authors: Liu, J.J.
Alipuly, A.
B?czek, T.
Wong, M.W. 
Žuvela, P. 
Keywords: Chromatography
Elution order prediction
Non-linear programming (NLP)
Quantitative structure-retention relationships (QSRR)
Reversed phaseliquid chromatography (RP-LC)
Issue Date: 2019
Publisher: MDPI AG
Citation: Liu, J.J., Alipuly, A., B?czek, T., Wong, M.W., Žuvela, P. (2019). Quantitative structure-retention relationships with non-linear programming for prediction of chromatographic elution order. International Journal of Molecular Sciences 20 (14) : 3443. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain betterperforming QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: International Journal of Molecular Sciences
ISSN: 1661-6596
DOI: 10.3390/ijms20143443
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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