Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: a review.
This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) and coupled to mass spectrometry (GC×GC-MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.
Institute of Chemistry, University of Campinas (Unicamp), Campinas, São Paulo, Brazil.
Leandro Wang
LW
Hantao
Helga Gabriela
HG
Aleme
Marcio Pozzobon
MP
Pedroso
Guilherme Post
GP
Sabin
Ronei Jesus
RJ
Poppi
Fabio
F
Augusto
D005591:Chemical Fractionation / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D016018:Least-Squares Analysis; D015999:Multivariate Analysis