Fast and accurate numerical method for predicting gas chromatography retention time

Claumann, Carlos Alberto; Zibetti, André Wüst; Bolzan, Ariovaldo; Machado, Ricardo Antônio Francisco; Pinto, Luciano Henrique

doi:10.1016/j.chroma.2015.06.004

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…While publications focused on estimating/improving estimations of thermodynamic parameters and predicting retention times in GC and GC × GC appeared over the years , only a few specifically included peak width prediction. In the early 2000s, Lomsugarit and coworkers divided the adjusted peak width, w R ′, into hold‐up width, w M , and unadjusted peak width, w R .…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

Stevenson

Harynuk

2019

J of Separation Science

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Thermodynamics-based models have been demonstrated to be useful for predicting retention time and peak widths in gas chromatography and two-dimensional gas chromatography separations. However, the collection of data to train the models can be time consuming, which lessens the practical utility of the method. In this contribution, a method for obtaining thermodynamic-based data to predict peak widths in temperature-programmed gas chromatography is presented. Experimental work to collect data for peak width prediction is identical to that required to collect data for retention time prediction using approaches that we have presented previously. Using this combined approach, chromatograms including retention times and peak widths are predicted with very high accuracy. Typical errors in retention time are < 0.5%, while errors in peak width are typically < 5% as demonstrated using polycycic aromatic hydrocarbons and a mixture containing compounds with aldehyde, ketone, alkene, alkane, alcohol, and ester functionalities. K E Y W O R D Speak width prediction, retention prediction, thermodynamic modeling As such, over the last few decades, researchers have developed methods and tools for predicting and simulating GC separations. Among the approaches to predictive modeling of GC separations, thermodynamic-based models have received a great deal of attention over the years. Much of

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Section: Introductionmentioning

confidence: 99%

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

Stevenson

Harynuk

2019

J of Separation Science

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“…There are a variety of methods existing in the literature that have been used to predict the retention times of analytes and/or enable transfer of retention data between columns [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. These methods typically relate retention times to thermodynamic parameters, retention indices or structural molecular descriptors (as is the case with qualitative structure retention relationship, QSRR, models).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, thermodynamic methods are much more amenable to modeling comprehensive 2-D GC (GC × GC) separations when compared to the isovolatility curve generation methods that are required for use of the retention index in the second dimension of a GC × GC separation [7,19]. In the literature, a number of studies to predict retention time using thermodynamic-based models have been reported [1,5,7,[10][11][12][20][21][22][23][24][25]. However, there is room for improvement, especially with regard to translation of retention time predictions across columns (of the same stationary phase chemistry) with varying geometries, and translation between different instruments.…”

Section: Introductionmentioning

confidence: 99%

“…These methods typically relate retention times to thermodynamic parameters, retention indices or structural molecular descriptors (as is the case with qualitative structure retention relationship, QSRR, models). Among the various methods, thermodynamic‐based modeling of retention time, wherein thermodynamic properties are related to the partition coefficient, has attracted much attention in recent years . Thermodynamic‐based methods have many advantages over other techniques.…”

Section: Introductionmentioning

confidence: 99%

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A simple, fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters

Hou

Stevenson

Harynuk

2018

J of Separation Science

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The transfer of retention times based on thermodynamic models between columns can aid in separation optimization and compound identification in gas chromatography. Although earlier investigations have been reported, this problem remains unsuccessfully addressed. One barrier is poor predictive accuracy when moving from a reference column or system to a new target column or system. This is attributed to challenges associated with the accurate determination of the effective geometric parameters of the columns. To overcome this, we designed least squares-based models that account for geometric parameters of the columns and thermodynamic parameters of compounds as they partition between mobile and stationary phases. Quasi-Newton-based algorithms were then used to perform the numerical optimization. In this first of three parts, the model used to determine the geometric parameters of the reference column and the thermodynamic parameters of compounds subjected to separation is introduced. As will be shown, the overall approach significantly improves the predictive accuracy and transferability of thermodynamic data (and retention times) between columns of the same stationary phase chemistry. The data required for the determination of the thermodynamic parameters and retention time prediction are obtained from fast and simple experiments. The proposed model and optimization algorithms were tested and validated using simulated and experimental data.

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“…This model was used to investigate the interaction between analyte and carrier gas inside capillary column with a specific stationary phase, expressed by the relationship between ∆G and k. The temperature dependence of ∆G can be established by the basic thermodynamic relationship as mentioned in equation 7 [28][29][30].…”

Section: Logk = Ee + Ss + Aa + Bb + Ll + C (1)mentioning

confidence: 99%

Development of retention index based simulation for validation of compound identification in GC?GC

Kakanopas

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Comprehensive two-dimensional gas chromatography (GC?GC) is a high-performance technique for separation, identification and quantification of volatiles and semi-volatiles in complex multi-component samples such as biomolecular molecules, essential oil, foods, and petroleum. One of the most popular detectors used for peak identification with GC?GC is mass spectrometer (MS) allowing identification of separated peaks based on comparison with mass spectral library. However, only MS library comparison shows low confidence in compound identifications due to the fact that compounds with similar structures (especially for isomers) often have similar mass spectra. Apart from sample preparation, a great challenge is to effectively select types of stationary phase and experimental condition for improved separation of each sample (i.e. column selection, temperature program, modulation period and hold up time). This research established the computational approach to simulate GC?GC results by using first and second dimensional retention index (1I and 2I) based calculation approach is established to simulate retention times (1tR and 2tR) and contour plots of samples from (GC?GC-MS). For the result without 1tR and 2tR data of alkane references (1tR(n) and 2tR(n)), the following steps were applied: (1) curve fitting based on van den Dool and Kratz relationship in order to simulate 1tR(n) using a training set of volatile compounds in a sample with their experimental 1tR data, and (2) simulation of 2tR(n) at different 1tR(n) to construct their isovolatility curves based on a nonlinear equation with six constants. These parameters were obtained by performing curve fitting according to the experimental 2tR data of the same training set. Simulation of 1tR and 2tR of target analytes (1tR,sim and 2tR,sim) with known 1I and 2I were performed using 1tR(n) and the simulated isovolatility curves. Gaussian equations were then applied to generate the peak intensity profiles, and summation of peak profiles of all the analytes was performed in order to simulate the contour plot for each sample using MATLAB. All the calculations and curve fittings were carried out by using Solver in Microsoft Excel. The approach was applied to simulate results for 622 compounds in several samples including saffron (Crocus sativas L.), Boswellia papyrifera, acacia honey, incense powder/smoke and perfume. These were compared with the experimental data showing good correlation with the R2 of 0.975-0.999 and 0.449-0.992 for 1tR and 2tR, respectively. This approach was then applied to propose 10 compounds which may be incorrectly identified from the literatures based on the great differences between 2tR,sim and the experimental 2tR.

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Fast and accurate numerical method for predicting gas chromatography retention time

Cited by 9 publications

References 22 publications

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

Thermodynamics‐based modelling of gas chromatography separations across column geometries and systems, including the prediction of peak widths

A simple, fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters

Development of retention index based simulation for validation of compound identification in GC?GC

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