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Singh YR, Shah DB, Kulkarni M, Patel SR, Maheshwari DG, Shah JS, Shah S. Current trends in chromatographic prediction using artificial intelligence and machine learning. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2785-2797. [PMID: 37264667 DOI: 10.1039/d3ay00362k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.
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Affiliation(s)
- Yash Raj Singh
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Darshil B Shah
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Mangesh Kulkarni
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreyanshu R Patel
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Dilip G Maheshwari
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Jignesh S Shah
- Department of Pharmaceutical Regulatory Affairs, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreeraj Shah
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
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Mert Ozupek N, Cavas L. Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis. Anal Biochem 2021; 627:114248. [PMID: 34022188 DOI: 10.1016/j.ab.2021.114248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/24/2021] [Accepted: 05/07/2021] [Indexed: 10/21/2022]
Abstract
Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.
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Affiliation(s)
- Nazli Mert Ozupek
- Graduate School of Natural and Applied Sciences, Department of Biotechnology, Dokuz Eylül University, 35160, İzmir, Turkey
| | - Levent Cavas
- Graduate School of Natural and Applied Sciences, Department of Biotechnology, Dokuz Eylül University, 35160, İzmir, Turkey; Faculty of Sciences, Department of Chemistry, Dokuz Eylül University, 35390, İzmir, Turkey.
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3
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Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116188] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Biancolillo A, Maggi MA, Bassi S, Marini F, D’Archivio AA. Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution. Molecules 2020; 25:molecules25061262. [PMID: 32168813 PMCID: PMC7144001 DOI: 10.3390/molecules25061262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 03/01/2020] [Accepted: 03/11/2020] [Indexed: 12/02/2022] Open
Abstract
Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.
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Affiliation(s)
- Alessandra Biancolillo
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, Coppito, 67100 L’Aquila (AQ), Italy;
| | - Maria Anna Maggi
- Hortus Novus srl, Via Campo Sportivo 2, Canistro, 67100 L’Aquila, Italy;
| | - Sebastian Bassi
- Dipartimento di Chimica, Università degli Studi di Roma “La Sapienza”, Piazzale Aldo Moro 5, 00185 Roma, Italy; (S.B.); (F.M.)
| | - Federico Marini
- Dipartimento di Chimica, Università degli Studi di Roma “La Sapienza”, Piazzale Aldo Moro 5, 00185 Roma, Italy; (S.B.); (F.M.)
| | - Angelo Antonio D’Archivio
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, Coppito, 67100 L’Aquila (AQ), Italy;
- Correspondence:
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Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants. Int J Mol Sci 2019; 20:ijms20092120. [PMID: 31035726 PMCID: PMC6539345 DOI: 10.3390/ijms20092120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/23/2019] [Accepted: 04/25/2019] [Indexed: 12/01/2022] Open
Abstract
Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.
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Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients. Molecules 2019; 24:molecules24030632. [PMID: 30754702 PMCID: PMC6384946 DOI: 10.3390/molecules24030632] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 02/06/2019] [Accepted: 02/07/2019] [Indexed: 12/29/2022] Open
Abstract
A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data, taken from literature, were collected in acetonitrile⁻water eluents under application of linear organic modifier gradients ( gradients), pH gradients, or double pH/ gradients. At first, retention data collected in gradients and pH gradients were modeled separately, while these were successively combined in one dataset and fitted simultaneously. Specific ANN-based models were generated by combining the descriptors of the gradient profiles with 16 inputs representing the amino acids and providing the retention time of these solutes as the response. Categorical "bit-string" descriptors were adopted to identify the solutes, which allowed simultaneously modeling the retention times of all 16 target amino acids. The ANN-based models tested on external gradients provided mean errors for the predicted retention times of 1.1% ( gradients), 1.4% (pH gradients), 2.5% (combined and pH gradients), and 2.5% (double pH/ gradients). The accuracy of ANN prediction was better than that previously obtained by fitting of the same data with retention models based on the solution of the fundamental equation of gradient elution.
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D'Archivio AA, Di Donato F, Foschi M, Maggi MA, Ruggieri F. UHPLC Analysis of Saffron ( Crocus sativus L.): Optimization of Separation Using Chemometrics and Detection of Minor Crocetin Esters. Molecules 2018; 23:molecules23081851. [PMID: 30044436 PMCID: PMC6222919 DOI: 10.3390/molecules23081851] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/20/2018] [Accepted: 07/22/2018] [Indexed: 02/06/2023] Open
Abstract
Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components.
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Affiliation(s)
- Angelo Antonio D'Archivio
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Via Vetoio, 67100 L'Aquila, Italy.
| | - Francesca Di Donato
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Via Vetoio, 67100 L'Aquila, Italy.
| | - Martina Foschi
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Via Vetoio, 67100 L'Aquila, Italy.
| | | | - Fabrizio Ruggieri
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Via Vetoio, 67100 L'Aquila, Italy.
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Han S, Song Y, Guan H, Chen T, Chi Y, Deng H. A new selection principle for model compounds in quantitative structure-retention relationship by HPLC for the determination of n
-octanol/water partition coefficients of bisphenols. SEPARATION SCIENCE PLUS 2018. [DOI: 10.1002/sscp.201800041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Shuying Han
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
| | - Yilin Song
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
| | - Hanyu Guan
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
| | - Tao Chen
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
| | - Yumei Chi
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
| | - Haishan Deng
- College of Pharmacy; Nanjing University of Chinese Medicine; Nanjing China
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Taheri M, Bagheri M, Moazeni-Pourasil RS, Ghassempour A. Response surface methodology based on central composite design accompanied by multivariate curve resolution to model gradient hydrophilic interaction liquid chromatography: Prediction of separation for five major opium alkaloids. J Sep Sci 2017; 40:3602-3611. [DOI: 10.1002/jssc.201700416] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/10/2017] [Accepted: 07/09/2017] [Indexed: 11/05/2022]
Affiliation(s)
- Mohammadreza Taheri
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
| | - Mohsen Bagheri
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
| | | | - Alireza Ghassempour
- Medicinal Plants and Drugs Research Institute; Shahid Beheshti University; Tehran Iran
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Zisi C, Sampsonidis I, Fasoula S, Papachristos K, Witting M, Gika HG, Nikitas P, Pappa-Louisi A. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression. Metabolites 2017; 7:metabo7010007. [PMID: 28208794 PMCID: PMC5372210 DOI: 10.3390/metabo7010007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 02/05/2017] [Indexed: 01/07/2023] Open
Abstract
Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered.
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Affiliation(s)
- Chrysostomi Zisi
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
- Correspondence: ; Tel.: +30-231-099-7765
| | - Ioannis Sampsonidis
- Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK;
| | - Stella Fasoula
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Konstantinos Papachristos
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Michael Witting
- Helmholtz Zentrum München, Research Unit Analytical BioGeoChemistry, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany;
| | - Helen G. Gika
- Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Panagiotis Nikitas
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Adriani Pappa-Louisi
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
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Taheri M, Moazeni-Pourasil RS, Sheikh-Olia-Lavasani M, Karami A, Ghassempour A. Central composite design with the help of multivariate curve resolution in loadability optimization of RP-HPLC to scale-up a binary mixture. J Sep Sci 2016; 39:1031-40. [PMID: 26768981 DOI: 10.1002/jssc.201500526] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Revised: 12/24/2015] [Accepted: 12/24/2015] [Indexed: 11/10/2022]
Abstract
Chromatographic method development for preparative targets is a time-consuming and subjective process. This can be particularly problematic because of the use of valuable samples for isolation and the large consumption of solvents in preparative scale. These processes could be improved by using statistical computations to save time, solvent and experimental efforts. Thus, contributed by ESI-MS, after applying DryLab software to gain an overview of the most effective parameters in separation of synthesized celecoxib and its co-eluted compounds, design of experiment software that relies on multivariate modeling as a chemometric approach was used to predict the optimized touching-band overloading conditions by objective functions according to the relationship between selectivity and stationary phase properties. The loadability of the method was investigated on the analytical and semi-preparative scales, and the performance of this chemometric approach was approved by peak shapes beside recovery and purity of products.
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Affiliation(s)
- Mohammadreza Taheri
- Medicinal Plants and Drug Research Institute, Shahid Beheshti University, Tehran, Iran
| | | | | | | | - Alireza Ghassempour
- Medicinal Plants and Drug Research Institute, Shahid Beheshti University, Tehran, Iran
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Barron LP, McEneff GL. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods. Talanta 2016; 147:261-70. [DOI: 10.1016/j.talanta.2015.09.065] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 09/22/2015] [Accepted: 09/27/2015] [Indexed: 12/01/2022]
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Stanstrup J, Neumann S, Vrhovšek U. PredRet: prediction of retention time by direct mapping between multiple chromatographic systems. Anal Chem 2015; 87:9421-8. [PMID: 26289378 DOI: 10.1021/acs.analchem.5b02287] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.
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Affiliation(s)
- Jan Stanstrup
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) , Via E. Mach 1, 38010 San Michele all'Adige, Trentiono, Italy
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry , Weinberg 3, 06120 Halle, Germany
| | - Urška Vrhovšek
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) , Via E. Mach 1, 38010 San Michele all'Adige, Trentiono, Italy
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Munro K, Miller TH, Martins CP, Edge AM, Cowan DA, Barron LP. Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data. J Chromatogr A 2015; 1396:34-44. [DOI: 10.1016/j.chroma.2015.03.063] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/27/2015] [Accepted: 03/23/2015] [Indexed: 02/07/2023]
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Eugster PJ, Boccard J, Debrus B, Bréant L, Wolfender JL, Martel S, Carrupt PA. Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling. PHYTOCHEMISTRY 2014; 108:196-207. [PMID: 25457501 DOI: 10.1016/j.phytochem.2014.10.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 10/02/2014] [Accepted: 10/08/2014] [Indexed: 06/04/2023]
Abstract
The detection and early identification of natural products (NPs) for dereplication purposes require efficient, high-resolution methods for the profiling of crude natural extracts. This task is difficult because of the high number of NPs in these complex biological matrices and because of their very high chemical diversity. Metabolite profiling using ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC–HR-MS) is very efficient for the separation of complex mixtures and provides molecular formula information as a first step in dereplication. This structural information alone or even combined with chemotaxonomic information is often not sufficient for unambiguous metabolite identification. In this study, a representative set of 260 NPs containing C, H, and O atoms only was analysed in generic UHPLC–HR-MS profiling conditions. Two easy to use quantitative structure retention relationship (QSRR) models were built based on the measured retention time and on eight simple physicochemical parameters calculated from the structures. First, an original approach using several partial least square (PLS) regressions according to the phytochemical classes provided satisfactory results with an easy calculation. Secondly, a unique artificial neural network (ANN) model provided similar results on the whole set of NPs but required dedicated software. The retention prediction methods described in this study were found to improve the level of confidence of the identification of given analytes among putative isomeric structures. Its applicability was verified for the dereplication of NPs in model plant extracts.
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Miller TH, Musenga A, Cowan DA, Barron LP. Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks. Anal Chem 2013; 85:10330-7. [DOI: 10.1021/ac4024878] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Thomas H. Miller
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Alessandro Musenga
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - David A. Cowan
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Leon P. Barron
- Analytical & Environmental Sciences Division, School of Biomedical Sciences, King’s College London, 150 Stamford Street, London SE1 9NH, United Kingdom
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Sousa IJ, Ferreira MJU, Molnár J, Fernandes MX. QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity. Eur J Pharm Sci 2013; 48:542-53. [DOI: 10.1016/j.ejps.2012.11.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2012] [Revised: 10/23/2012] [Accepted: 11/17/2012] [Indexed: 10/27/2022]
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18
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D’Archivio AA, Giannitto A, Maggi MA, Ruggieri F. Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling. Anal Chim Acta 2012; 717:52-60. [DOI: 10.1016/j.aca.2011.12.047] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Revised: 12/18/2011] [Accepted: 12/21/2011] [Indexed: 11/16/2022]
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19
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D’Archivio AA, Maggi MA, Ruggieri F. Multi-variable retention modelling in reversed-phase high-performance liquid chromatography based on the solvation method: A comparison between curvilinear and artificial neural network regression. Anal Chim Acta 2011; 690:35-46. [DOI: 10.1016/j.aca.2011.01.056] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2010] [Revised: 12/29/2010] [Accepted: 01/27/2011] [Indexed: 11/17/2022]
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