Rapid discrimination of bergamot essential oil by paper spray mass spectrometry and chemometric analysis. (30th August 2016)

• Record Type: Journal Article
• Authors: Taverna, Domenico; Di Donna, Leonardo; Mazzotti, Fabio; Tagarelli, Antonio; Napoli, Anna; Furia, Emilia; Sindona, Giovanni
• Other Names: Giorgi Gianluca guestEditor.
• Is Part Of: Journal of mass spectrometry. Volume 51:Number 9(2016)
• Page Start: 761
• Page End: 767
• Publication Date: 2016-08-30
• Subjects: ambient mass spectrometry -- paper spray -- bergamot essential oil -- chemometric analysis; Mass spectrometry -- Periodicals; 543.65
• DOI: 10.1002/jms.3820
• Languages: English
• ISSNs: 1076-5174

Abstract

Abstract : A novel approach for the rapid discrimination of bergamot essential oil from other citrus fruits oils is presented. The method was developed using paper spray mass spectrometry (PS‐MS) allowing for a rapid molecular profiling coupled with a statistic tool for a precise and reliable discrimination between the bergamot complex matrix and other similar matrices, commonly used for its reconstitution. Ambient mass spectrometry possesses the ability to record mass spectra of ordinary samples, in their native environment, without sample preparation or pre‐separation by creating ions outside the instrument. The present study reports a PS‐MS method for the determination of oxygen heterocyclic compounds such as furocoumarins, psoralens and flavonoids present in the non‐volatile fraction of citrus fruits essential oils followed by chemometric analysis. The volatile fraction of Bergamot is one of the most known and fashionable natural products, which found applications in flavoring industry as ingredient in beverages and flavored foodstuff. The development of the presented method employed bergamot, sweet orange, orange, cedar, grapefruit and mandarin essential oils. PS‐MS measurements were carried out in full scan mode for a total run time of 2 min. The capability of PS‐MS profiling to act as marker for the classification of bergamot essential oils was evaluated by using multivariate statistical analysis. Two pattern recognition techniques, linear discriminant analysis and soft independent modeling of class analogy, were applied to MS data. The cross‐validation procedure has shown excellent results in terms of the prediction ability because both models have correctly classified all samples for each category. Copyright © 2016 John Wiley & Sons, Ltd.