Extension of the two-dimensional mass channel cluster plot method to fast separations utilizing low thermal mass gas chromatography with time-of-flight mass spectrometry

Fitz, Brian D.; Synovec, Robert E.

doi:10.1016/j.aca.2016.01.045

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…Since the MCR-ALS models were generated without constraints in this study, improvement to R s * may be achieved if suitable constraints can be readily implemented, such as initial peak-profile estimates for the more challenging overlap situations. For example, the previously reported mass cluster method ,, shows promise for extracting peak profiles with an R s * of ∼0.05, but further development is warranted.…”

Section: Discussionmentioning

confidence: 99%

Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography–Mass Spectrometry

et al. 2017

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A new approach is presented to determine the probability of achieving a successful quantitative analysis for gas chromatography coupled with mass spectrometry (GC-MS). The proposed theory is based upon a probabilistic description of peak overlap in GC-MS separations to determine the probability of obtaining a successful quantitative analysis, which has its lower limit of chromatographic resolution R at some minimum chemometric resolution, R*; that is to say, successful quantitative analysis can be achieved when R ≥ R*. The value of R* must be experimentally determined and is dependent on the chemometric method to be applied. The approach presented makes use of the assumption that analyte peaks are independent and randomly distributed across the separation space or are at least locally random, namely, that each analyte represents an independent Bernoulli random variable, which is then used to predict the binomial probability of successful quantitative analysis. The theoretical framework is based on the chromatographic-saturation factor and chemometric-enhanced peak capacity. For a given separation, the probability of quantitative success can be improved via two pathways, a chromatographic-efficiency pathway that reduces the saturation of the sample and a chemometric pathway that reduces R* and improves the chemometric-enhanced peak capacity. This theory is demonstrated through a simulation-based study to approximate the resolution limit, R*, of multivariate curve resolution-alternating least-squares (MCR-ALS). For this study, R* was determined to be ∼0.3, and depending on the analytical expectations for the quantitative bias and the obtained mass-spectral match value, a lower value of R* ∼ 0.2 may be achievable.

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Section: Discussionmentioning

confidence: 99%

Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography–Mass Spectrometry

et al. 2017

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Bayesian approach to peak deconvolution and library search for high resolution gas chromatography – Mass spectrometry

Barcaru

Mol

Tienstra

et al. 2017

Analytica Chimica Acta

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A novel probabilistic Bayesian strategy is proposed to resolve highly coeluting peaks in high-resolution GC-MS (Orbitrap) data. Opposed to a deterministic approach, we propose to solve the problem probabilistically, using a complete pipeline. First, the retention time(s) for a (probabilistic) number of compounds for each mass channel are estimated. The statistical dependency between m/z channels was implied by including penalties in the model objective function. Second, Bayesian Information Criterion (BIC) is used as Occam's razor for the probabilistic assessment of the number of components. Third, a probabilistic set of resolved spectra, and their associated retention times are estimated. Finally, a probabilistic library search is proposed, computing the spectral match with a high resolution library. More specifically, a correlative measure was used that included the uncertainties in the least square fitting, as well as the probability for different proposals for the number of compounds in the mixture. The method was tested on simulated high resolution data, as well as on a set of pesticides injected in a GC-Orbitrap with high coelution. The proposed pipeline was able to detect accurately the retention times and the spectra of the peaks. For our case, with extremely high coelution situation, 5 out of the 7 existing compounds under the selected region of interest, were correctly assessed. Finally, the comparison with the classical methods of deconvolution (i.e., MCR and AMDIS) indicates a better performance of the proposed algorithm in terms of the number of correctly resolved compounds.

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Examination of the two-dimensional mass channel cluster plot method for gas chromatography – mass spectrometry in the context of the statistical model of overlap

Berrier

Reaser

Pinkerton

et al. 2019

Journal of Chromatography A

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Extension of the two-dimensional mass channel cluster plot method to fast separations utilizing low thermal mass gas chromatography with time-of-flight mass spectrometry

Cited by 4 publications

References 56 publications

Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography–Mass Spectrometry

Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography–Mass Spectrometry

Bayesian approach to peak deconvolution and library search for high resolution gas chromatography – Mass spectrometry

Examination of the two-dimensional mass channel cluster plot method for gas chromatography – mass spectrometry in the context of the statistical model of overlap

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