title: Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods.
abstract: Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography-mass spectrometry (GC-MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC-MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC-MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some chromatographic regions. Each chromatographic segment for eighteen samples is column-wise augmented with m/z values as common mode to preserve bilinear model assumption needed for MCR analysis. Extended multivariate curve resolution alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. After finding the best MCR-ALS model, the relative concentrations for resolved components are examined using principal component analysis (PCA) and k-nearest neighbor (KNN) clustering methods to explore similarities and dissimilarities among different citrus samples according to their secondary metabolites. In general, four clear-cut clusters are determined and the chemical markers (chemotypes) responsible to this differentiation are characterized by subsequent discriminate analysis using counter-propagation artificial neural network (CPANN) method. It is concluded that the use of proposed strategy is a more reliable and faster way for the analysis of large data sets like chromatographic fingerprints of natural products compared to conventional methods.
authors: Department of Chemistry, Faculty of Science, University of Isfahan, Isfahan 81746-73441, Iran. Electronic address: h.parastar@sci.ui.ac.ir.
authors: Hadi
authors: H
authors: Parastar
authors: Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.
authors: Mehdi
authors: M
authors: Jalali-Heravi
authors: Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran.
authors: Hassan
authors: H
authors: Sereshti
authors: Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.
authors: Ahmad
authors: A
authors: Mani-Varnosfaderani
mesh_terms: D000465:Algorithms; D002957:Citrus / Q000737:chemistry*; D005638:Fruit / Q000737:chemistry*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D013223:Statistics as Topic