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

Abstract

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.

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

“…The retention times of six non‐coeluting compounds of the calibration mixture, on the reference column, along with the separation conditions were input into our MATLAB code that estimates the thermodynamic parameters (A, B, and C) of the components and the optimized inner diameter of the column (as experienced by the probe molecules of the mixture) . The compounds chosen were decane, 1‐octanol, undecane, 2,6‐dimethylaniline, methyl decanoate, and methyl laurate.…”

“…The thermodynamics‐based parameters of these seven compounds were estimated using our current methods . Briefly, the retention times of seven PAHs on the reference column were obtained under the conditions described in the Reference System portion of the Instrumentation section above.…”

“…Additionally, we demonstrated that we can achieve accurate predictions across columns of different geometries on the system used to acquire the thermodynamic parameters (errors < 2%) . More recently, improvements to our previous optimization algorithms have allowed us to accurately predict retention times on new (target) columns of a given stationary phase even when columns are of different geometries and installed in different instruments than the column on which the thermodynamic parameters were collected (reference column) . The work presented here introduces the prediction of peak widths to our models.…”

“…However, before these thermodynamic parameters may be used across columns, it is necessary to calibrate both the reference column (the column from which the thermodynamic parameters are obtained) and the target column (the column on which retention times will be predicted). Our approach to estimating these parameters, calibration of the columns and subsequent prediction of retention time is described in previous publications . The same general approach is applied here to estimate the thermodynamic‐based parameters for the prediction of peak width.…”

New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells

“…105 When microliter volume samples can yield hundreds of thousands of features, 106 which may represent billions of currently unknown molecules, a paradigm shift is required in order to comprehensively and unambiguously identify all the molecules in complex samples. Therefore, there is growing interest within the metabolomics community, and among funding agencies, 107 to develop methods and tools that accurately predict experimental properties of metabolites encountered in MS-based metabolomics analyses, such as retention times, [108][109][110][111][112] MS/MS spectra, [113][114][115][116][117] collision cross sections (CCS), [118][119][120] etc. Ultimate success from these efforts will be an analytical approach that provides unambiguous identification of the complete set of detected molecules in a sample.…”