Quantitative Structure–Retention Relationship Study of a Variety of Compounds in Reversed-Phase Liquid Chromatography: A PLS-MLR-STANN Approach

2008 ◽  
Vol 27 (2) ◽  
pp. 137-146 ◽  
Author(s):  
M. Jalali-Heravi ◽  
Z. Garkani-Nejad ◽  
A. Kyani
Keyword(s):  
Liquid Chromatography ◽  
Reversed Phase ◽  
Quantitative Structure ◽  
Reversed Phase Liquid Chromatography ◽  
Phase Liquid ◽  
Structure Retention ◽  
Relationship Study ◽  
Quantitative Structure Retention Relationship

scholarly journals Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order

2019 ◽  
Vol 20 (14) ◽  
pp. 3443 ◽  
Author(s):  
Liu ◽  
Alipuly ◽  
Bączek ◽  
Wong ◽  
Žuvela
Keyword(s):  
Linear Programming ◽  
Case Studies ◽  
Retention Time ◽  
Reversed Phase ◽  
Elution Order ◽  
Quantitative Structure ◽  
Reversed Phase Liquid Chromatography ◽  
Non Linear Programming ◽  
Phase Liquid ◽  
Structure Retention

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 better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

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