@article{Quiroz-Moreno2020,
author = {Quiroz-Moreno, Cristian and Furlan, Mayra Fontes and de Souza, Jo{\~{a}}o Raul Belinato and Augusto, Fabio and Alexandrino, Guilherme L. and Mogoll{\'{o}}n, Noroska G.S.},
doi = {10.1016/j.microc.2020.104830},
file = {:home/quiroz/Downloads/10.1016@j.microc.2020.104830.pdf:pdf},
issn = {0026265X},
journal = {Microchemical Journal},
keywords = {Comprehensive two-dimensional gas chromatography,Exploratory analysis,Metabolomics,Signal processing,Toolbox},
number = {March},
pages = {104830},
publisher = {Elsevier},
title = {{RGCxGC toolbox: an R-package for data processing in comprehensive two-dimensional gas chromatography-mass spectrometry}},
url = {https://doi.org/10.1016/j.microc.2020.104830},
volume = {156},
year = {2020}
}
@article{Skov2008,
abstract = {A multipurpose sampler (Gerstel MPS), designed for liquid large volume, gaseous and headspace samples was used for the GC–MS analysis of organic volatiles in human urine. Headspace sampling with a volume-, temperature- and speed-controlled gas-tight syringe was combined with a temperature-controlled cold injection system (CIS) for cold trapping, enrichment and focusing of analytes. Regular 2-ml GC vials filled with 1 ml acidified urine were used as headspace sampling vials. A 100-vial autosampler tray was equipped with an additional temperature and heating time controlled “preheating station” for five vials. Profiles of organic volatiles in human urine were determined and 34 components identified. Trimethylamine (TMA) and 4-heptanone as two metabolites of medical interest were quantified. Calibration curves and intra assay imprecision for 4-heptanone concentrations in the range of 40 to 800 ng/ml showed a correlation coefficient of r=0.9980 and a relative standard deviation (RSD) between 3.0 and 3.4{\%}. Calibration curves and intra-assay imprecision for TMA concentrations in the range of medical interest from 0.5 to 20 $\mu$g/ml showed a correlation coefficient of r=0.9968 and a RSD between 4.1 and 6.8{\%}. The high practicability of the multipurpose sampler for both gaseous and liquid samples together with the here shown good reproducibility and sensitivity make this single CIS-GC–MS system very attractive for routine clinical use in metabolic profiling of organic volatiles (headspace) and non-volatiles (liquid).},
author = {Skov, Thomas and Bro, Rasmus},
doi = {10.1007/s00216-007-1618-z},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Skov, Bro - 2008 - Solving fundamental problems in chromatographic analysis(2).pdf:pdf},
isbn = {0021600716},
issn = {16182642},
journal = {Analytical and Bioanalytical Chemistry},
number = {1},
pages = {281--285},
pmid = {17957360},
title = {{Solving fundamental problems in chromatographic analysis}},
volume = {390},
year = {2008}
}
@article{Eilers2003,
abstract = {The well-known and popular Savitzky-Golay filter has several disadvantages. A very attractive alternative is a smoother based on penalized least squares, extending ideas presented by Whittaker 80 years ago. This smoother is extremely fast, gives continuous control over smoothness, interpolates automatically, and allows fast leave-one-out cross-validation. It can be programmed in a few lines of Matlab code. Theory, implementation, and applications are presented.},
author = {Eilers, Paul H.C.},
doi = {10.1021/ac034173t},
isbn = {0003-2700},
issn = {00032700},
journal = {Analytical Chemistry},
number = {14},
pages = {3631--3636},
pmid = {14570219},
title = {{A perfect smoother}},
volume = {75},
year = {2003}
}
@article{Tomasi2004,
abstract = {Two different algorithms for time-alignment as a preprocessing step$\backslash$nin linear factor models are studied. Correlation optimized warping$\backslash$nand dynamic time warping are both presented in the literature as$\backslash$nmethods that can eliminate shift-related artifacts from measurements$\backslash$nby correcting a sample vector towards a reference. In this study$\backslash$nboth the theoretical properties and the practical implications of$\backslash$nusing signal warping as preprocessing for chromatographic data are$\backslash$ninvestigated. The connection between the two algorithms is also discussed.$\backslash$nThe findings are illustrated by means of a case study of principal$\backslash$ncomponent analysis on a real data set, including manifest retention$\backslash$ntime artifacts, of extracts from coffee samples stored under different$\backslash$npackaging conditions for varying storage times. We concluded that$\backslash$nfor the data presented here dynamic time warping with rigid slope$\backslash$nconstraints and correlation optimized warping are superior to unconstrained$\backslash$ndynamic time warping; both considerably simplify interpretation of$\backslash$nthe factor model results. Unconstrained dynamic time warping was$\backslash$nfound to be too flexible for this chromatographic data set, resulting$\backslash$nin an overcompensation of the observed shifts and suggesting the$\backslash$nunsuitability of this preprocessing method for this type of signals.$\backslash$nCopyright {\textcopyright} 2004 John Wiley {\&} Sons, Ltd.},
author = {Tomasi, Giorgio and {Van Den Berg}, Frans and Andersson, Claus},
doi = {10.1002/cem.859},
isbn = {0886-9383},
issn = {08869383},
journal = {Journal of Chemometrics},
keywords = {COW,DTW,PCA,Retention time shift,Warping},
number = {5},
pages = {231--241},
pmid = {26061853},
title = {{Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data}},
volume = {18},
year = {2004}
}
@article{Prebihalo2018,
abstract = {SCOPE OF REVIEW Analysis of volatile and semi-volatile analytes by gas chromatography (GC) methods is an indispensable tool in the analytical chemist's tool box. A myriad of fields of study rely upon the application of GC methods to address an ever growing demand to provide useful chemical information from GC data. As the realm of GC application has expanded, there has been an evolution to develop more powerful instrumental and data analysis approaches to keep pace with the wealth of complex samples that require analysis. To address this challenge, advances in GC instrumentation having evolved from one-dimensional gas chromatography (1D-GC) and heart cutting approaches such as (GC-GC), to instrumentation referred to broadly as multidimensional gas chromatography (MDGC), which can take on many forms. The principle form of MDGC that has gained wide implementation is comprehensive two-dimensional (2D) gas chromatography (GC × GC) as shown in Figure 1A, pioneered nearly 26 years ago by Liu and Phillips.1 When comparing 1D-GC (Figure 1B) relative to GC × GC (Figure 1C), the benefits of a secondary separation become evident. Blumberg and co-workers have theoretically determined that the 2D peak capacity provided by GC × GC compared to the peak capacity of 1D-GC is approximately an order of magnitude higher when the run times are held constant.2 This benefit is illustrated using a relatively complex sample of coffee. By adding a multivariate detector such as a time-of-flight mass spectrometry (TOFMS), another selective dimension of data is provided which may allow identification of analytes (Figure 1D). This review will focus essentially on GC × GC, with selected developments of other forms of MDGC also covered. In this regard, we focus primarily on research published since the last Fundamental Review published by Seeley and Seeley in 2013,3 with older publications covered as deemed necessary to provide additional insight into addressing the current challenges, and to put the more recent developments into historical context.},
author = {Prebihalo, Sarah E. and Berrier, Kelsey L. and Freye, Chris E. and Bahaghighat, H. Daniel and Moore, Nicholas R. and Pinkerton, David K. and Synovec, Robert E.},
doi = {10.1021/acs.analchem.7b04226},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Prebihalo et al. - 2018 - Multidimensional Gas Chromatography Advances in Instrumentation, Chemometrics, and Applications(2).pdf:pdf},
issn = {15206882},
journal = {Analytical Chemistry},
number = {1},
pages = {505--532},
pmid = {29088543},
title = {{Multidimensional Gas Chromatography: Advances in Instrumentation, Chemometrics, and Applications}},
volume = {90},
year = {2018}
}
@article{Eilers2004,
abstract = {A parametric model is proposed for the warping function when aligning chromatograms. A very fast and stable algorithm results that consumes little memory and avoids the artifacts of dynamic time warping. The parameters of the warping function are useful for quality control. They also are easily interpolated, allowing alignment of batches of chromatograms based on warping functions for a limited number of calibration samples.},
author = {Eilers, Paul H.C.},
doi = {10.1021/ac034800e},
isbn = {0003-2700},
issn = {00032700},
journal = {Analytical Chemistry},
number = {2},
pages = {404--411},
pmid = {14719890},
title = {{Parametric Time Warping}},
volume = {76},
year = {2004}
}
@article{DabaoZhang2008,
abstract = {A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and second dimensions on the basis of applying a one-dimensional COW algorithm to characteristic vectors, nongrid points can be interpolatively warped. This 2-D algorithm was directly applied to total ion counts (TIC) chromatographic profiles of homogeneous chemical samples, i.e., samples including mostly identical compounds. For heterogeneous chemical samples, the 2-D algorithm is first applied to certain selected ion counts chromatographic profiles, and the resultant warping parameters are then used to warp the corresponding TIC chromatographic profiles. The developed 2-D COW algorithm can also be applied to align other 2-D separation images, e.g., LC x LC data, LC x GC data, GC x GC data, LC x CE data, and CE x CE data.},
author = {{Dabao Zhang} and {Xiaodong Huang} and {Fred E. Regnier} and {Min Zhang}},
doi = {10.1021/ac7024317},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dabao Zhang et al. - 2008 - Two-dimensional correlation optimized warping algorithm for aligning GCxGC-MS data.pdf:pdf},
isbn = {0003-2700},
issn = {00032700},
journal = {Analytical Chemistry},
number = {8},
pages = {2664--2671},
pmid = {18351753},
title = {{Two-dimensional correlation optimized warping algorithm for aligning GCxGC-MS data}},
volume = {80},
year = {2008}
}
@article{Wold1987,
abstract = {SUMMARY The Lohmoller-Wold decomposition of multi-way (three-way, four-way, etc.) data arrays is combined with the non-linear partial least squares (NIPALS) algorithms to provide multi-way solutions of principal components analysis (PCA) and partial least squares modelling in latent variables (PLS). The decomposition of a multi-way array is developed as the product of a score vector and a loading array, where the score vectors have the same properties as those of ordinary two-way PCA and PLS. In image analysis, the array would instead be decomposed as the product of a loading vector and an image score matrix. The resulting methods are equivalent to the method of unfolding a multi-way array to a two-way matrix followed by ordinary PCA or PLS analysis. This automatically proves the eigenvector and least squares properties of the multi-way PCA and PLS methods. The methodology is presented; the algorithms are outlined and illustrated with a small chemical example.},
author = {Wold, Svante and Geladi, Paul and Esbensen, Kim and {\"{O}}hman, Jerker},
doi = {10.1002/cem.1180010107},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wold et al. - 1987 - Multi-way principal components- and PLS-analysis.pdf:pdf},
issn = {0886-9383},
journal = {Journal of Chemometrics},
keywords = {Multi-way array,Multiorder array,Multivariate calibration,Principal components PLS},
number = {January 1986},
pages = {41--56},
title = {{Multi-way principal components- and PLS-analysis}},
volume = {1},
year = {1987}
}
@article{Reichenbach2004,
abstract = {Comprehensive two-dimensional gas chromatography (GC×GC) presents information technology challenges in data handling, visualization, processing, analysis and reporting. With its significantly greater separation capacity, GC×GC generates data sets that are two to three orders-of-magnitude larger than for conventional GC and that exhibit an order-of-magnitude or more distinct peaks in complex multidimensional patterns. GC×GC is a much richer source of data for chemical analyses, but extracting relevant information from the large, complex data sets requires advanced information technologies. This paper reviews a range of state-of-the-art information technologies for GC×GC, including graphical user-interfaces (GUIs), computer graphics for data visualization, data file formats for storage and interchange, digital image processing, image analysis and pattern recognition, data and information formats, and software architecture and engineering. {\textcopyright} 2004 Published by Elsevier B.V.},
author = {Reichenbach, Stephen E. and Ni, Mingtian and Kottapalli, Visweswara and Visvanathan, Arvind},
doi = {10.1016/j.chemolab.2003.12.009},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Reichenbach et al. - 2004 - Information technologies for comprehensive two-dimensional gas chromatography.pdf:pdf},
isbn = {0169-7439},
issn = {01697439},
journal = {Chemometrics and Intelligent Laboratory Systems},
keywords = {07.05.-t,07.05.Bx,07.05.Kf,07.05.Pj,07.05.Rm,07.05.Wr,82.80.Bg,Comprehensive two-dimensional gas chromatography,Computer interfaces,GC×GC,Image processing,Information technology,Visualization},
number = {2},
pages = {107--120},
title = {{Information technologies for comprehensive two-dimensional gas chromatography}},
volume = {71},
year = {2004}
}
@article{Nasstrom2018,
abstract = {Background},
author = {N{\"{a}}sstr{\"{o}}m, Elin and Jonsson, P{\"{a}}r and Johansson, Anders and Dongol, Sabina and Karkey, Abhilasha and Basnyat, Buddha and {Tran Vu Thieu}, Nga and {Trinh Van}, Tan and Thwaites, Guy E. and Antti, Henrik and Baker, Stephen},
doi = {10.1371/journal.pntd.0006215},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/N{\"{a}}sstr{\"{o}}m et al. - 2018 - Diagnostic metabolite biomarkers of chronic typhoid carriage.pdf:pdf},
isbn = {1111111111},
issn = {19352735},
journal = {PLoS Neglected Tropical Diseases},
number = {1},
pages = {1--15},
pmid = {29373578},
title = {{Diagnostic metabolite biomarkers of chronic typhoid carriage}},
volume = {12},
year = {2018}
}
@article{Szymanska2018,
abstract = {Efficient and reliable analysis of chemical analytical data is a great challenge due to the increase in data size, variety and velocity. New methodologies, approaches and methods are being proposed not only by chemometrics but also by other data scientific communities to extract relevant information from big datasets and provide their value to different applications. Besides common goal of big data analysis, different perspectives and terms on big data are being discussed in scientific literature and public media. The aim of this comprehensive review is to present common trends in the analysis of chemical analytical data across different data scientific fields together with their data type-specific and generic challenges. Firstly, common data science terms used in different data scientific fields are summarized and discussed. Secondly, systematic methodologies to plan and run big data analysis projects are presented together with their steps. Moreover, different analysis aspects like assessing data quality, selecting data pre-processing strategies, data visualization and model validation are considered in more detail. Finally, an overview of standard and new data analysis methods is provided and their suitability for big analytical chemical datasets shortly discussed.},
author = {Szyma{\'{n}}ska, Ewa},
doi = {10.1016/j.aca.2018.05.038},
file = {:home/quiroz/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Szyma{\'{n}}ska - 2018 - Modern data science for analytical chemical data – A comprehensive review.pdf:pdf},
isbn = {4204950671},
issn = {18734324},
journal = {Analytica Chimica Acta},
keywords = {Big data,Chemical analytical data,Chemometrics,Data science,Methodology},
title = {{Modern data science for analytical chemical data – A comprehensive review}},
volume = {1028},
year = {2018}
}
