Computer-assisted method development in liquid chromatography–mass spectrometry: New proposals

García-Lavandeira, J.; Losada, B.; Martínez-Pontevedra, J.A.; Lores, Marta; Cela, R.

doi:10.1016/j.chroma.2008.08.105

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…LC-MS-based methods for NTM provide quantitative analysis with a combination of selectivity and sensitivity and increase the possibility of identifying metabolites. ,, In LC-MS-based NTM analysis, a target peak displaying metabolomics information is selected and an accurate mass of the eluted metabolite is identified. This accurate mass is then used to define potential molecular formulas corresponding to the chosen metabolite peak by searching electronic resources, such as the Human Metabolome Database (HMDB), METLIN, LMSD, MassBank, RIKEN, and PubChem. , Based on the exact mass of the metabolite of interest, potential candidates can be found from the databases, and the returned matches then can be identified and confirmed using additional experimental data.…”

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Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

et al. 2018

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Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

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Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

et al. 2018

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“…Isocratic retention times, peak widths, and symmetry factors were copied and pasted into the PREGA database module. All further data handling during the optimization processes was carried out by use of the PREGA v 6.0 software package (5,14).…”

Section: Chromatographic Optimization and Data Handlingmentioning

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A Binary-Like Approach for the Computer Assisted Method Development of Isocratic and Programmed Ternary Solvent Elutions in Reversed-Phase Liquid Chromatography

García-Lavandeira¹,

Martínez-Pontevedra²,

Cela³

2011

Journal of Chromatographic Science

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A specific tool to enable exploration of multisolvent isocratic and programmed elutions for the computer assisted method development of reversed-phase liquid chromatography separations is described. The tool is purposely identical to those used in the optimization of binary solvent systems, which are by far the most commonly used by chromatographers. Existing data from failed binary solvent optimization processes are reused to explore ternary solvent systems with a few additional isocratic and programmed runs. This allows the development of efficient retention models for ternary systems, although the work of the chromatographer remains identical to that for optimization of binary systems. The retention models are used to develop an unattended optimization process and finally, the chromatographer selects the most satisfactory solution for testing and implementing in routine analysis. The process is exemplified with a mixture of 12 compounds that cannot be separated satisfactorily in aqueous binary solvent systems with methanol and acetonitrile as modifiers.

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Computerized Optimization of Liquid Chromatography Separations

Torrijos

Romero

2017

Encyclopedia of Analytical Chemistry

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Computer‐assisted methods development (CAMD) of LC (liquid chromatography) separations comprises the strategies, methodologies, and software that can be used for more effective and efficient development of separations needed in the analytical chromatography laboratory. Substitution of costly and time‐consuming experiments by simulation, based on accurate regression models, provides a quick and convenient mode of testing hypothesis, verification, and reaching accurate conclusions in a period of seconds or minutes rather days or weeks of patient experimentation. Having been developed during the past four decades, CAMD provides actually several approaches that tackle separation problems both for mixtures in which all compounds are known (so the position of peaks in the experiments can be tracked) and for mixtures having a number of unknown components or components that cannot be tracked accurately. A chromatographer can select among a variety of more or less intuitive objective functions to adapt the performance of CAMD procedures to its goals. Software tools have made use of the advances in informatics and computation to provide user‐friendly and efficient graphical interfaces, relieving the chromatographer from making complex calculations, statistical testing of the reliability of simulations, and facilitating the interpretation of results. CAMD is not, and was never, intended to replace the chromatographers but to bring tools to make their work more efficient and facilitate the work of less‐experienced ones. Conveniently used, CAMD tools can help the analyst do a better job in a record time.

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Computer-assisted method development in liquid chromatography–mass spectrometry: New proposals

Cited by 12 publications

References 28 publications

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

A Binary-Like Approach for the Computer Assisted Method Development of Isocratic and Programmed Ternary Solvent Elutions in Reversed-Phase Liquid Chromatography

Computerized Optimization of Liquid Chromatography Separations

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