TY - JOUR
T1 - RGCxGC toolbox
T2 - An R-package for data processing in comprehensive two-dimensional gas chromatography-mass spectrometry
AU - Quiroz-Moreno, Cristian
AU - Furlan, Mayra Fontes
AU - Belinato, João Raul
AU - Augusto, Fabio
AU - Alexandrino, Guilherme L.
AU - Mogollón, Noroska Gabriela Salazar
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7
Y1 - 2020/7
N2 - Comprehensive two-dimensional gas chromatography (GC×GC) offers detailed chemical information about volatile and semivolatile analytes from complex samples. However, the high complexity of the data structure encourages the development of new tools for a more efficient data handling and analysis. Although some tools have already been presented to overcome this challenge, there is still need for improvement. In this manuscript, we present a toolbox containing a pipeline for end-to-end basic GC×GC data processing which can be used for both, signal pre-processing and multivariate data analysis. The pre-processing algorithms perform signal smoothing, baseline correction, and peak alignment, while the multivariate analysis is done through Multiway Principal Component Analysis (MPCA). The software is capable to prepare the chromatographic data for further applications with other chemometric tools, e.g.: cluster analysis, regression, discriminant analysis, etc. The performance of this new software was tested on in-house experimental dataset and on two other published datasets.
AB - Comprehensive two-dimensional gas chromatography (GC×GC) offers detailed chemical information about volatile and semivolatile analytes from complex samples. However, the high complexity of the data structure encourages the development of new tools for a more efficient data handling and analysis. Although some tools have already been presented to overcome this challenge, there is still need for improvement. In this manuscript, we present a toolbox containing a pipeline for end-to-end basic GC×GC data processing which can be used for both, signal pre-processing and multivariate data analysis. The pre-processing algorithms perform signal smoothing, baseline correction, and peak alignment, while the multivariate analysis is done through Multiway Principal Component Analysis (MPCA). The software is capable to prepare the chromatographic data for further applications with other chemometric tools, e.g.: cluster analysis, regression, discriminant analysis, etc. The performance of this new software was tested on in-house experimental dataset and on two other published datasets.
KW - Comprehensive two-dimensional gas chromatography
KW - Exploratory analysis
KW - Metabolomics
KW - Signal processing
KW - Toolbox
UR - http://www.scopus.com/inward/record.url?scp=85081729964&partnerID=8YFLogxK
U2 - 10.1016/j.microc.2020.104830
DO - 10.1016/j.microc.2020.104830
M3 - Artículo
AN - SCOPUS:85081729964
SN - 0026-265X
VL - 156
JO - Microchemical Journal
JF - Microchemical Journal
M1 - 104830
ER -