Multiwavelength detection and reiterative least squares resolution of overlapped liquid chromatographic peaks

Frans, S. D.; McConnell, M. L.; Harris, Joel M.

doi:10.1021/ac00285a013

Cited by 40 publications

(20 citation statements)

References 52 publications

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“…Least squares unimodal regression is important, as currently either ad hoc or very restrictive methods are used in chemometrics for enforcing unimodality. [7][8][9][10] One approach often used in iterative algorithms is to simply change elements corresponding to local maxima on an estimated curve so that the local maxima disappear. Clearly such a method does not have any least squares or other well-defined properties.…”

Section: Introductionmentioning

confidence: 99%

Least squares algorithms under unimodality and non-negativity constraints

Bro¹,

Sidiropoulos²

1998

J. Chemometrics

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In this paper a least squares method is developed for minimizing jjY À XB T jj 2 F over the matrix B subject to the constraint that the columns of B are unimodal, i.e. each has only one peak, and jjMjj 2 F being the sum of squares of all elements of M. This method is directly applicable in many curve resolution problems, but also for stabilizing other problems where unimodality is known to be a valid assumption. Typical problems arise in certain types of time series analysis such as chromatography or flow injection analysis. A fundamental and surprising result of this work is that unimodal least squares regression (including optimization of mode location) is not any more difficult than two simple Kruskal monotone regressions. This had not been realized earlier, leading to the use of either undesirable ad hoc methods or very time-consuming exhaustive search algorithms. The new method is useful in and exemplified with two-and multi-way methods based on alternating least squares regression solving problems from fluorescence spectroscopy and flow injection analysis.

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Section: Introductionmentioning

confidence: 99%

Least squares algorithms under unimodality and non-negativity constraints

Bro¹,

Sidiropoulos²

1998

J. Chemometrics

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“…However, because the peaks are symmetrical and relatively well separated, resolution by fitting of Gaussian profiles (or modifications) may be a solution in these cases. By calculating the residuals by the reiterative least squares approach, information available in the mass spectra will still be utilised to find the best solutions [41,42].…”

Section: Discussionmentioning

confidence: 99%

“…(1)- (3) can also be applied together with peak models, e.g. variants of Gaussian peaks, by using the reiterative least squares principle [41,42].…”

Section: Principles Of Multivariate Deconvolutionmentioning

confidence: 99%

Quantification of linolenic acid isomers by gas chromatography‐mass spectrometry and deconvolution of overlapping chromatographic peaks

Mjøs¹

2004

Euro J Lipid Sci & Tech

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Quantification of linolenic acid isomers by gas chromatography-mass spectrometry and deconvolution of overlapping chromatographic peaksThe use of a technique for deconvolution of overlapping chromatographic peaks, Gentle, has been evaluated for quantification of alpha linolenic acid isomers analysed by gas chromatography-mass spectrometry. Mixtures containing varying amounts of linolenic acid methyl ester isomers with two or three trans double bonds were analysed by two different temperature programs. Overlapping chromatographic peaks were resolved by Gentle, and the areas of the resolved peaks were compared with reference values calculated by use of internal standards. The results show that the small differences that exist between the mass spectra of the analysed isomers are sufficient to achieve deconvolution of severely overlapping peaks. The errors were larger than seen for quantification of chromatographically resolved peaks. Especially for small peaks in a peak cluster, the errors relative to the peak size were large.

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“…For example, if multivariate detectors such as mass spectrometers and diode array absorbance detectors were used one might resolve peaks that are closer together [2, 16] thus effectively mitigating some of the loss due to under-sampling and possibly effectively decreasing α. Similarly, digital curve resolution techniques which assume a peak shape function might make it possible to resolve peaks that have resolution values less than 0.5 [17–25]. …”

Section: Introductionmentioning

confidence: 99%

The impact of sampling time on peak capacity and analysis speed in on-line comprehensive two-dimensional liquid chromatography

Potts

Stoll

et al. 2010

Journal of Chromatography A

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Comprehensive two-dimensional liquid chromatography (2DLC) offers a number of practical advantages over optimized one-dimensional LC in peak capacity and thus in resolving power. The traditional "product rule" for overall peak capacity for a 2DLC system significantly overestimates peak capacity because it neglects under-sampling of the first dimension separation. Here we expand on previous work by more closely examining the effects of the first dimension peak capacity and gradient time, and the second dimension cycle times on the overall peak capacity of the 2DLC system. We also examine the effects of re-equilibration time on under-sampling as measured by the under-sampling factor and the influence of molecular type (peptide vs. small molecule) on peak capacity. We show that in fast 2D separations (less than one hour), the second dimension is more important than the first dimension in determining overall peak capacity and conclude that extreme measures to enhance the first dimension peak capacity are usually unwarranted. We also examine the influence of sample types (small molecules vs. peptides) on second dimension peak capacity and peak capacity production rates, and how the sample type influences optimum second dimension gradient and re-equilibration times.

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Multiwavelength detection and reiterative least squares resolution of overlapped liquid chromatographic peaks

Cited by 40 publications

References 52 publications

Least squares algorithms under unimodality and non-negativity constraints

Least squares algorithms under unimodality and non-negativity constraints

Quantification of linolenic acid isomers by gas chromatography‐mass spectrometry and deconvolution of overlapping chromatographic peaks

The impact of sampling time on peak capacity and analysis speed in on-line comprehensive two-dimensional liquid chromatography

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