Chemometric analysis of comprehensive two dimensional gas chromatography–mass spectrometry metabolomics data

Izadmanesh, Yahya; Garreta-Lara, Elba; Ghasemi, Jahan B.; Lacorte, Sı́lvia; Matamoros, Víctor; Tauler, Romà

doi:10.1016/j.chroma.2017.01.052

Cited by 51 publications

(26 citation statements)

References 45 publications

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“…Reprinted with permission from [178]. Copyright 2012 American Chemical Society ALS was most suitable [180]. MCR-ALS was also applied by Omar et al for resolving co-eluting compounds in GC×GC-MS data from Cannabis sativa extracts [181].…”

Section: F I G U R E 14 Example Showing the Difference Between A Trilmentioning

confidence: 99%

“…Year Reference MCR tutorial MCR-ALS 2014 [172] Deconvolution of overlapping spectral polymer signals in SEC by MCR-ALS MCR-ALS 2014 [176] Application MCR-ALS coeluting compounds GC × GC analysis of Cannabis Sativa MCR-ALS 2014 [181] Methods for initial guess in MCR-ALS MCR-ALS, Comparison methods initial guess 1996 [174] KSFA for initial guess in MCR-ALS MCR-ALS, KSFA 1982 [175] Comparison PARFAC and MCR methods on GC × GC MCR-ALS, PARFAC 2017 [180] Comparison PARFAC and MCR methods on LC × LC MCR-ALS, PARFAC 2016 [179] Simplisma for initial guess in MCR-ALS MCR-ALS, SIMPLISMA 1991 [173] Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in GC × GC NLLSCF 2011 [59] Optimization Title…”

Section: Title Subcategorymentioning

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: F I G U R E 14 Example Showing the Difference Between A Trilmentioning

confidence: 99%

Section: Title Subcategorymentioning

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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show abstract

“…On the one hand, this makes possible to differentiate between samples using established pattern recognition or classification methods, such as PCA or PLS‐DA multivariate approaches. On the other hand, the quantitative information can be used to obtain possible markers related to the factor under study using methods such as statistical inference tests or the selected VIP variables obtained in the PLS‐DA model …”

Section: Peak Detection and Profilingmentioning

confidence: 99%

“…Although MCR‐ALS is initially only based on the fulfillment of the bilinear model explained above, it has been extended to model multiway data fulfilling multilinear models . Thus, MCR‐ALS can be used in most of the situations encountered in practice in multidimensional chromatography, when multilinear models are not fulfilled (only bilinear model is considered), when they are only partially fulfilled by some of the components, or when they are fulfilled by all the components of systems.…”

Section: Peak Detection and Profilingmentioning

confidence: 99%

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Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

et al. 2018

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The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis.

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Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists

Tauler

Parastar²

2022

Angewandte Chemie

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This Review summarizes how big (bio)chemical data (BBCD) can be analyzed with multivariate chemometric methods and highlights some of the important challenges faced by modern analytical researches. Here, the potential of chemometric methods to solve BBCD problems that are being encountered in chromatographic, spectroscopic and hyperspectral imaging measurements will be discussed, with an emphasis on their applications to omics sciences. In addition, insights and perspectives on how to address the analysis of BBCD are provided along with a discussion of the procedures necessary to obtain more reliable qualitative and quantitative results. In this Review, the importance of "big data" and of their relevance to (bio)chemistry are first discussed. Thereafter, analytical tools which can produce BBCD are presented as well as the theoretical background of chemometric methods and their limitations when they are applied to BBCD. Finally, the importance of chemometric methods for the analysis of BBCD in different chemical disciplines is highlighted with some examples. In this work, we have tried to cover many of the current applications of big data analysis in the (bio)chemistry field.

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Chemometric analysis of comprehensive two dimensional gas chromatography–mass spectrometry metabolomics data

Cited by 51 publications

References 45 publications

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

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

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists

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