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Pattern recognition analysis of chromatographic fingerprints of Crocus sativus L. secondary metabolites towards source identification and quality control

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Abstract

Chromatographic fingerprinting is an effective methodology for authentication and quality control of herbal products. In the presented study, a chemometric strategy based on multivariate curve resolution–alternating least squares (MCR–ALS) and multivariate pattern recognition methods was used to establish a gas chromatography–mass spectrometry (GC–MS) fingerprint of saffron. For this purpose, the volatile metabolites of 17 Iranian saffron samples, collected from different geographical regions, were determined using the combined method of ultrasound-assisted solvent extraction (UASE) and dispersive liquid–liquid microextraction (DLLME), coupled with GC–MS. The resolved elution profiles and the related mass spectra obtained by an extended MCR–ALS algorithm were then used to estimate the relative concentrations and to identify the saffron volatile metabolites, respectively. Consequently, 77 compounds with high reversed match factors (RMFs > 850) were successfully determined. The relative concentrations of these compounds were used to generate a new data set which was analyzed by multivariate data analysis methods including principal component analysis (PCA) and k-means. Accordingly, the saffron samples were categorized into five classes using these techniques. The results revealed that 11 compounds, as biomarkers of saffron, contributed to the class discrimination and characterization. Eleven biomarkers including nine secondary metabolites of saffron (safranal, α- and β-isophorone, phenylethyl alcohol, ketoisophorone, 2,2,6-trimethyl-1,4-cyclohexanedione, 2,6,6-trimethyl-4-oxo-2-cyclohexen-1-carbaldehyde, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one, and 2,6,6-trimethyl-4-hydroxy-1-cyclohexene-1-carboxaldehyde (HTCC)), a primary metabolite (linoleic acid), and a long chain fatty alcohol (nanocosanol) were distinguished as the saffron fingerprint. Finally, the individual contribution of each biomarker to the classes was determined by the counter propagation artificial neural network (CPANN) method.

General framework of the purposed pattern recognition method for saffron

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Acknowledgments

The study was carried out as a part of the research project titled “Development of an index for evaluation of Iranian saffron quality based on chemical constituents in its chromatographic fingerprints: combination of gas chromatograpy and novel multivariate chemometric methods” supported by Iran national science foundation (INSF) (http://www.insf.org). The authors would like thank to Mr. Mohammad-Ali Shoshtari and Mr. Ali Mokhtarian for their great help in supplying saffron samples from different plantation sites in Iran.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Science, University of Tehran, P.O.Box 14155-64555, Tehran, Iran

    Ghazaleh Aliakbarzadeh & Hassan Sereshti

  2. Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran

    Hadi Parastar

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  1. Ghazaleh Aliakbarzadeh
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  2. Hassan Sereshti
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Correspondence to Hassan Sereshti.

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Aliakbarzadeh, G., Sereshti, H. & Parastar, H. Pattern recognition analysis of chromatographic fingerprints of Crocus sativus L. secondary metabolites towards source identification and quality control. Anal Bioanal Chem 408, 3295–3307 (2016). https://doi.org/10.1007/s00216-016-9400-8

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