Mutual peak matching in a series of HPLC–DAD mixture analyses

Bogomolov, Andrey; McBrien, Michael

doi:10.1016/s0003-2670(03)00667-6

Cited by 23 publications

(9 citation statements)

References 18 publications

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“…Accurate automatic peak detection and, possibly, deconvolution of overlapping peaks are required before peaks can be "tracked" or labeled (but not necessarily be identified). Several peak-tracking algorithms have been developed for LC-DAD data, such as those of Round et al [290] and Bogomolov and McBrien [291]. However, hyphenation of LC to MS often is needed to properly track peaks.…”

Section: Peak Tracking and Alignmentmentioning

confidence: 99%

“…Bayesian peak tracking in GC × GC GC × GC 2016 [295] Peak-tracking algorithm for use in automated interpretive method-development tools in LC LC 2018 [294] Combined use of algorithms for peak picking, peak tracking, and retention modelling LC 2011 [293] Feature detection and alignment of LC-MS data using Kalman tracking LC 2008 [292] Mutual peak alignment in series of HPLC-DAD mixture analytes LC 2003 [291] Peak tracking of peptides in RP-LC using DAD data RP-LC 1994 [290] Method transfer Application of GC × GC combined with pixel-based chemometric processing for the chemical profiling of illicit drug samples COW application 2008 [64] Application of parallel computing to speed up chemometrics for GC × GC-TOFMS based metabolic fingerprinting Parallel computing 2011 [65] Chemometrics-assisted method development in RP-LC Review 2013 [185] Recent developments in GC × GC Review 2006 [192] Tutorial on Bayesian chemometrics Review 2007 [96] The 'PRISMA' mobile phase optimization model in TLC TLC 1985 [198] data set and comparisons with other approaches supported by numerical data have rarely been reported. A comprehensive study of different types of data and data-analysis techniques would allow a better overview of which techniques can be best used in which situation.…”

Section: Subcategory Year Referencementioning

confidence: 99%

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Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as highresolution mass spectrometry, causes an urgent need for highly efficient dataanalysis and optimization strategies. This is especially true for comprehensive twodimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from oneand two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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Section: Peak Tracking and Alignmentmentioning

confidence: 99%

Section: Subcategory Year Referencementioning

confidence: 99%

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Bos

Knol

Molenaar

et al. 2020

J of Separation Science

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“…The IKSFA method was developed by Malinowski and is a preferred set selection method that assumes the purest spectra in the data set are mutually more dissimilar than the mixture spectra [24]. IKSFA is an iterative improvement over KSFA which seeks to find the minimum number ( N ) of spectra (out of IJK total spectra) required to represent the entire data set through the characterization of the most orthogonal spectra that typify the original data matrix [25,26].…”

Section: Theorymentioning

confidence: 99%

Chemometric resolution and quantification of four-way data arising from comprehensive 2D-LC-DAD analysis of human urine

Bailey

Rutan

2011

Chemometrics and Intelligent Laboratory Systems

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Two-dimensional liquid chromatography (LC×LC) is quickly becoming an important technique for the analysis of complex samples, owing largely to the relatively high peak capacities attainable by this analytical technique. With the increase in the complexity of the sample comes a corresponding increase in the complexity of the collected data. Thus the need for chemometric methods capable of resolving and quantifying such data is ever more urgent in order to obtain the maximum information available from the data. To this end, we have developed a chemometric method that combines iterative key set factor analysis and multivariate curve resolution-alternating least squares analysis with a spectral selectivity constraint that is shown to be capable of resolving chromatographically rank deficient, non-multilinear data. (Spectrally rank deficient compounds can only be quantified if the peaks having the same spectra are chromatographically resolved.) Over 50 chromatographic peaks were found in a relatively small section of a LC×LC-diode array data set of replicate urine samples (a four-way data set) using the developed method. The relative concentrations for 34 of the 50 peaks were determined with % RSD values ranging from 0.09 % to 16 %.

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“…The identification of singlets is best made with a hyphenated dimension providing additional information and augmented by spectral libraries, e. g., MS or UV -Vis spectroscopy. Such identification precludes the complications of peak matching, by which the locations of compounds in different peaks must be obtained [27,28]. A comparison of n-column separations to multidimensional separations is warranted.…”

Section: General Comments On N-column Separationsmentioning

confidence: 99%

Validation of theory of n‐column separations with gas chromatograms predicted by commercial software

Williamson

Davis²

2007

J of Separation Science

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A probability theory for the average number of compounds resolved by the partial separation of complex mixtures on n columns was tested using commercial-software predictions of gas chromatograms. Such n-column separations are traditional means for addressing peak overlap, in which one chooses additional columns of different selectivity to separate compounds that cannot be separated by a single column. Gas chromatograms of five types of complex mixtures containing from 99 to 283 compounds were predicted for eight stationary phases using both optimized and other temperature programs. The number n of columns for different mixtures varied from 2 to 5. The numbers of compounds separated as singlet peaks at different resolution thresholds were compared to predictions, as evaluated with point-process statistical-overlap theory based on a Poisson distribution. A good agreement between theory and results was found in all cases corresponding to low saturation. Both good and poor agreements were found for cases corresponding to high saturation. A good agreement also was found for results based on resolving complex mixtures by a single column subject to two temperature programs. The moments and distribution of the number of resolved compounds were computed by Monte Carlo simulation, thus gauging the significance of departures between results and theory. The potential of such simulations to explore the limitations of theory was briefly investigated.

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Mutual peak matching in a series of HPLC–DAD mixture analyses

Cited by 23 publications

References 18 publications

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

Chemometric resolution and quantification of four-way data arising from comprehensive 2D-LC-DAD analysis of human urine

Validation of theory of n‐column separations with gas chromatograms predicted by commercial software

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