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Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods

Parastar, H ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1016/j.chroma.2012.06.011
  3. Publisher: 2012
  4. Abstract:
  5. 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
  6. Keywords:
  7. Chemometrics ; Chromatographic fingerprinting ; Citrus fruits ; Classification ; Gas chromatography-mass spectrometry ; Multivariate curve resolution ; Asymmetric peak ; Bilinear models ; Chemical markers ; Chemometric method ; Chemotypes ; Chromatographic fingerprints ; Clustering methods ; Common mode ; Conventional methods ; Counter propagation ; Data sets ; Discriminate analysis ; GC-MS analysis ; K-nearest neighbors ; Large datasets ; Mass spectral ; MCR-ALS ; Multivariate curve resolution ; Multivariate curve resolution alternating least-squares ; Natural products ; Relative concentration ; Retention time ; Secondary metabolites ; Z value ; Classification (of information) ; Curve fitting ; Gas chromatography ; Metabolites ; Neural networks ; Principal component analysis ; Natural product ; Artificial neural network ; Chemical analysis ; Chemometric analysis ; Citrus fruit ; Cluster analysis ; Column chromatography ; Concentration (parameters) ; Elution ; k nearest neighbor ; Linear system ; Mass fragmentography ; Metabolite ; Multivariate analysis ; Nonhuman ; Principal component analysis ; Priority journal ; Regression analysis ; Reliability ; Statistical model ; Algorithms ; Fruit ; Statistics as Topic ; Citrus
  8. Source: Journal of Chromatography A ; Volume 1251 , 2012 , Pages 176-187 ; 00219673 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0021967312008771