title: Resolving of challenging gas chromatography-mass spectrometry peak clusters in fragrance samples using multicomponent factorization approaches based on polygon inflation algorithm.
abstract: Analysis of fragrance composition is very important for both the fragrance producers and consumers. Unraveling of fragrance formulation is necessary for quality control, competitor and trace analysis. Gas chromatography-mass spectrometry (GC-MS) has been introduced as the most appropriate analytical technique for this type of analysis, which is based on Kovats index and MS database. The most straightforward method to analyze a GC-MS dataset is to integrate those peaks that can be recognized by their mass profiles. But, because of common problems of chromatographic data such as spectral background, baseline offset and specially overlapped peaks, accurate quantitative and qualitative analysis could be failed. Some chemometric modeling techniques such as bilinear multivariate curve resolution (MCR) methods have been introduced to overcome these problems and obtained well resolved chromatographic profiles. The main drawback of these methods is rotational ambiguity or nonunique solution that is represented as area of feasible solutions (AFS). Polygonal inflation algorithm (PIA) is an automatic and simple to use algorithm for numerical computation of AFS. In this study, the extent of rotational ambiguity in curve resolution methods is calculated by MCR-BAND toolbox and the PIA. The ability of the PIA in resolving GC-MS data sets is evaluated by simulated GC-MS data in comparison with other popular curve resolution methods such as multivariate curve resolution alternative least square (MCR-ALS), multivariate curve resolution objective function minimization (MCR-FMIN) by different initial estimation methods and independent component analysis (ICA). In addition, two typical challenging area of total ion chromatogram (TIC) of commercial fragrances with overlapped peaks were analyzed by the PIA to investigate the possibility of peak deconvolution analysis.
authors.affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Iran.
authors.forename: Salehe
authors.initials: S
authors.lastname: Ghaheri
authors.affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Iran. Electronic address: masoum@kashanu.ac.ir.
authors.forename: Saeed
authors.initials: S
authors.lastname: Masoum
authors.affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Gholami
mesh_terms: D000465:Algorithms; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry; D016018:Least-Squares Analysis; D010476:Perfume / Q000737:chemistry*
chemical_list: D010476:Perfume
keywords: Fragrance; Gas chromatography–mass spectrometry; Multivariate curve resolution; Polygonal inflation algorithm; Rotational ambiguity