Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

Hantao, Leandro Wang; Aleme, Helga Gabriela; Pedroso, Márcio Pozzobon; Sabin, Guilherme P.; Poppi, Ronei J.; Augusto, Fábio

doi:10.1016/j.aca.2012.04.003

Cited by 66 publications

(41 citation statements)

References 129 publications

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“…These data pose some challenges to data processing algorithms, mainly because the elution profiles are not constant from run to run. This demands two alternative solutions: correcting for the temporal changes by synchronizing the chromatograms (160-162), or applying a calibration methodology that is able to model the elution profile changes, such as MCR-ALS (163).…”

Section: Second-order Multivariate Calibration Examplesmentioning

confidence: 99%

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Arancibia

Damiani

Ibáñez

et al. 2014

Journal of AOAC INTERNATIONAL

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This report reviews recent literature on the application of multivariate calibration techniques to both first- and second-order data, aimed at the analytical determination of analytes of interest or sample properties in a variety of industrial, pharmaceutical, food, and environmental samples, including examples of process control. The most used data processing tools are briefly described, with emphasis on the advantages that can be obtained by applying specific combinations of multivariate data and algorithms. The main focus is on works devoted to first-order data (i.e., spectra, chromatograms, etc.) combined with partial least-squares regression, which has become the standard for this type of analytical research. A brief discussion on recent work on second-order data and algorithms is also included, as this field is rapidly growing, although at present it does not show, the general applicability of the first-order counterparts.

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Section: Second-order Multivariate Calibration Examplesmentioning

confidence: 99%

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Arancibia

Damiani

Ibáñez

et al. 2014

Journal of AOAC INTERNATIONAL

View full text Add to dashboard Cite

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“…However, determination of the window for the calculation is difficult or even impossible when high level of noise and background exist in the signals [19]. Thus, the problem limits the application of WFA in the analysis of real samples [13]. In this work, a generalized window was proposed for analysis of GC-MS signals using WFA.…”

Section: Introductionmentioning

confidence: 98%

“…However, the determination of the window is difficult for signals with high level of noise and background. Moreover, since the results by these methods are strongly dependent on the window, bias may arise if an incorrect window is provided [13].…”

Section: Introductionmentioning

confidence: 99%

Generalized window factor analysis for selective analysis of the target component in real samples with complex matrices

Cai

Shao

2015

Journal of Chromatography A

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“…Recently a similar method to AMDIS, GAVIN was developed as a freely available software (BEHRENDS et al, 2011). Another approach is based on multivariate curve resolution (MCR) (LAWTON & SYLVESTRE, 1971;DE JUAN & TAULER, 2006) that decomposes three-way GC-MS data and allow spectral deconvolution and estimation of metabolite concentrations via modelling (HANTAO et al, 2012).…”

Section: Data Pre-processing Prior To Chemometric Analysismentioning

confidence: 99%

Trends in the application of chemometrics to foodomics studies

Khakimov¹,

Gürdeniz²,

Engelsen³

2015

Acta Alimentaria

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There is an ever-increasing trend in advanced food analysis and foodomics to use more and more sophisticated analytical platforms that generate large and complex data structures, which in turn require more and more sophisticated data analysis tools for converting data into information. The choice of multivariate chemometric methods is primarily determined by the design of the study, type of the data, and the conclusions sought. In order to validate multivariate models, scientists are required to have basic chemometric knowledge and to be familiar with the variance structure of the investigated data. This review outlines some of the key aspects of applying common chemometric methods used within foodomics and provides selected examples of current applications. The review aims to provide simple insight into various multivariate methods and to illustrate pros and cons of unsupervised and supervised methods. The main analytical platforms used in foodomics are briefl y discussed from the application point of view and the utilization of the generated data is illustrated. In addition, advanced data pre-processing tools, prior to multivariate analysis, are explained and relevant tools are demonstrated.

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Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

Cited by 66 publications

References 129 publications

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Recent Applications of First- and Second-Order Multivariate Calibration to Analytical Chemistry

Generalized window factor analysis for selective analysis of the target component in real samples with complex matrices

Trends in the application of chemometrics to foodomics studies

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