Computer Resolution of Unresolved Convoluted Gas-Chromatographic Peaks

Anderson, Andrew H.; Gibb, T. C.; Littlewood, A. B.

doi:10.1093/chromsci/8.11.640

Cited by 97 publications

(30 citation statements)

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“…Within a factor, peaks are detected by using first and second derivatives to find local minima and maxima. All found peaks are then simultaneously fit to mathematically idealized forms; we assume a Gaussian curve as the ideal chromatographic peak shape (Anderson et al, 1970), though experimental peaks are often perturbed from the ideal shape by instrumental factors and a certain level of mixing of signals is introduced (Isaacman-VanWertz et al, 2017).…”

Section: Decision Treementioning

confidence: 99%

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Kim

Lerner

Sueper

et al. 2022

Preprint

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Abstract. Environmental samples typically contain hundreds or thousands of unique organic compounds, and even minor components may provide valuable insight into their sources and transformations. To understand atmospheric processes, individual components are frequently identified and quantified using gas chromatography/mass spectrometry. However, due to the complexity and frequently variable nature of such data, data reduction is a significant bottleneck in analysis. Consequently, only a subset of known analytes is often reported for a dataset, and a large amount of potentially useful data are discarded. We present here an automated approach of cataloging and potentially identifying all analytes in a large chromatographic dataset and demonstrate the utility of our approach in an analysis of ambient aerosols. We use a coupled factor analysis/decision tree approach to de-convolute peaks and comprehensively catalog nearly all analytes in a dataset. Positive Matrix Factorization (PMF) of small sub-sections of multiple chromatograms is applied to extract factors that represent chromatographic profiles and mass spectra of potential analytes, in which peaks are detected. A decision tree based on peak shape, noise, retention time, and mass spectrum is applied to discard erroneous peaks and combine peaks determined to represent the same analyte. With our approach, all analytes within the small section of the chromatogram are cataloged, and the process is repeated for overlapping sections across the chromatogram, generating a complete list of the retention times and estimated mass spectra of all peaks in a dataset. We validate this approach using samples of known compounds and demonstrate the separation of poorly resolved peaks with similar mass spectra and the resolution of peaks that appear in only a fraction of chromatograms. As a case study, this method is applied to a complex real-world dataset of the composition of atmospheric particles, in which more than 1100 analytes are resolved.

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Section: Decision Treementioning

confidence: 99%

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Kim

Lerner

Sueper

et al. 2022

Preprint

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“…In a review of methods of digital data analysis, Marson (625) and Metzger (647) treated a few methods of fused peak deconvolution and peak area reallocation. Computer resolution of unresolved peaks was shown to be enhanced by fast Fourier transform analysis of Gaussian ( 586) and asymmetrical peaks (641), the numerical method described by Goldberg (861), an iterative nonlinear regression analysis with a skewed Gaussian (24,26), an algebraic analysis of overlapping peaks (185), and the four parameter, skewed Gaussian method (425). Other workers have investigated more classical methods such as the perpendicular drop (708), and the derivative peak height ratio method ( 510).…”

Section: Detectorsmentioning

confidence: 99%

Gas chromatography

Cram¹,

Juvet²

1972

Anal. Chem.

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review surveys developments in the field of gas chromatography since publication of the last review in this series (496) and covers the years 1970-71. Gas chromatography continues to be one of the most active areas in analytical chemistry. A recent report by the American Chemical Society ( 791) is based on the employment specialty choices of 84,925 chemists and chemical engineers who reported to the 1970 National Register of Scientific and Technical Personnel, an activity of

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“…Curve fitting is one of the most utilized techniques for the resolution of overlapped peaks, but reliable and accurate initial estimations of the number of peaks, their positions, shapes, and widths are necessary to find acceptable solutions. [18][19][20] Also, the resolving ability is limited by noise level. 21 One of the main drawbacks is that as the number of overlapping peaks increases, either small errors in the data (e.g., noise or baseline distortions), or errors in the mathematical model can be magnified, leading to large errors in the parameters of the final model 22,23 and to ambiguous fitting results.…”

Section: Introductionmentioning

confidence: 99%

Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis

Olazábal

Prasad

Stark

et al. 2004

Analyst

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A new procedure for resolving noisy overlapped peaks in DNA separations by capillary electrophoresis (CE) is developed. The procedure combines both a wavelet-based denoising method that effectively denoises the signal and a novel approximate deconvolution technique that resolves the fragment peaks and improves the ability to separate highly overlapped peaks early in the electrophoresis process. Different kinds of overlapped peaks with and without noise simulated by computer as well as some DNA experimental electropherograms were submitted to the new procedure. A second order differential operator with variable coefficients is applied to the entire electrophoresis signal at any given time and approximate deconvolutions of the individual Gaussian peaks are performed. The operator incorporates the effect of the superposition and gives exact annihilation in the neighborhood of each peak. Overlapped peaks with a resolution higher than 0.46 can be resolved directly. Also, the method can determine the peak components of signals with a signal to noise ratio higher than 1.4

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Computer Resolution of Unresolved Convoluted Gas-Chromatographic Peaks

Cited by 97 publications

References 0 publications

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Comprehensive detection of analytes in large chromatographic datasets by coupling factor analysis with a decision tree

Gas chromatography

Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis

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