Sectional moving window factor analysis for diagnosing elution chromatographic patterns

Zeng, Zhong Da; Xu, Cheng‐Jian; Liang, Yi; Li, Bo Yan

doi:10.1016/s0169-7439(03)00116-3

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Algorithms based on EFA can be used to follow the evolution of the rank in a data matrix and to estimate the concentration window of each component. In this work, the detection of these windows is accomplished by the sectional moving window factor analysis method that is able to identify the embedded elutions. The elution chromatographic pattern that was extracted by this method is in agreement with actual concentration windows of each component for the simulated data set.…”

Section: Resultsmentioning

confidence: 99%

“…To achieve this goal, it is essential to have local rank information. There are several methods to detect the local rank and selectivity based mostly on evolving factor analysis (EFA) and also heuristic evolving latent projection type of approaches . Despite the importance of the resolution theorems, they have not attracted much attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Investigation and visualization of resolution theorems in self modeling curve resolution (SMCR) methods

Akbari

Abdollahi

2013

Journal of Chemometrics

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Different approaches have been proposed during recent years to improve the solutions obtained by multivariate curve resolution methods, among them studies on circumstances that result in unique answers are of particular importance. Three so‐called resolution theorems proposed by Rolf Manne comprehensively discuss and survey these conditions. Despite the importance of these theorems, they have not attracted much attention in the literature. In this work, we have returned to the resolution theorems by using visualization tools for calculation and demonstration the area of feasible solutions in a simulated three component system. The effect of selectivity and local rank constraints on rotational ambiguity in the results of self modeling curve resolution methods is the focus. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation and visualization of resolution theorems in self modeling curve resolution (SMCR) methods

Akbari

Abdollahi

2013

Journal of Chemometrics

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“…8 2.16.7 EFA of Images, Two-Dimensional-EFA An exciting new development extends the idea of EFA to the analysis of hyperspectral images. Although this is within the reach of a PC, it is not a fast process.…”

Section: Exhaustive-efamentioning

confidence: 99%

Two-Way Data Analysis: Evolving Factor Analysis

Maeder

Juan

2009

Comprehensive Chemometrics

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“…Moving windows have been used in GC-MS for factor analysis [16,17,18,19]. In these studies, factor analysis techniques were applied trough a moving window with the aim of detecting components or spectral features.…”

Section: Introductionmentioning

confidence: 99%

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods

Domingo-Almenara

Perera

Brezmes

2016

Journal of Chromatography A

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Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis-orthogonal signal deconvolution (ICA-OSD) and multivariate curve resolution-alternating least squares (MCR-ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC-qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA-OSD and MCR-ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R coefficients of determination exhibited a high correlation (R>0.90) in both ICA-OSD and MCR-ALS moving window-based approaches.

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Sectional moving window factor analysis for diagnosing elution chromatographic patterns

Cited by 10 publications

References 14 publications

Investigation and visualization of resolution theorems in self modeling curve resolution (SMCR) methods

Investigation and visualization of resolution theorems in self modeling curve resolution (SMCR) methods

Two-Way Data Analysis: Evolving Factor Analysis

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods

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