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

被引:38
作者
Parastar, Hadi [1 ]
Jalali-Heravi, Mehdi [2 ]
Sereshti, Hassan [3 ]
Mani-Varnosfaderani, Ahmad [2 ]
机构
[1] Univ Isfahan, Fac Sci, Dept Chem, Esfahan 8174673441, Iran
[2] Sharif Univ Technol, Dept Chem, Tehran, Iran
[3] Univ Tehran, Fac Sci, Dept Chem, Tehran, Iran
关键词
Chemometrics; Multivariate curve resolution; Chromatographic fingerprinting; Classification; Gas chromatography-mass spectrometry; Citrus fruits; MULTIVARIATE CURVE RESOLUTION; ESSENTIAL OILS; VOLATILE COMPONENTS; LIQUID-CHROMATOGRAPHY; NEURAL NETWORKS; QUALITY-CONTROL; PARADISI L; LEMON; QUANTIFICATION; FLAVONOIDS;
D O I
10.1016/j.chroma.2012.06.011
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:176 / 187
页数:12
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