Planta Med 2018; 84(14): 1045-1054
DOI: 10.1055/a-0585-5987
Natural Product Chemistry and Analytical Studies
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Application of GC/Q-ToF Combined with Advanced Data Mining and Chemometric Tools in the Characterization and Quality Control of Bay Leaves

Mei Wang
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
,
Vijayasankar Raman
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
,
Jianping Zhao
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
,
Bharathi Avula
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
,
Yan-Hong Wang
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
,
Philip L. Wylie
2  Agilent Technologies, Wilmington, DE, USA
,
Ikhlas A. Khan
1  National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, MS, USA
3  Division of Pharmacognosy, Department of BioMolecular Sciences, School of Pharmacy, University of Mississippi, University, MS, USA
› Author Affiliations
Further Information

Publication History

received 01 November 2017
revised 13 February 2018

accepted 26 February 2018

Publication Date:
14 March 2018 (online)

Abstract

Correct identification of the true bay leaf (Laurus nobilis) and its substitutes is important not only for the quality control of the products, but also for the safety of the consumers. L. nobilis is often substituted or confused with other species, such as Cinnamomum tamala, Pimenta racemosa, Syzygium polyanthum, and Umbellularia californica. In the present study, the potential of gas chromatography combined with quadrupole time-of-flight mass spectrometry for the profiling of various bay leaf products was evaluated for the first time. Thirty-nine authenticated samples representing the true bay leaf and the four commonly substituted species were analyzed. An automatic feature extraction algorithm was applied for data mining and pretreatment in order to identify the most characteristic compounds representing different bay leaf groups. This set of data was employed to construct a sample class prediction model based on stepwise reduction of data dimensionality followed by principal component analysis and partial least squares discriminant analysis. The statistical model, with demonstrated excellent accuracies in recognition and prediction abilities, enabled the correct classification of commercial samples including complex mixtures and essential oils. In addition, in-house developed personal compound database and library with retention time locking offered the advantage of combining retention time matching with accurate mass matching, resulting in high confidence of compound identification for each bay leaf subgroup. At least three marker compounds were identified for each bay leaf species that could be used to discriminate among them.

Supporting Information