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Identification of adulteration in botanical samples with untargeted metabolomics

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Abstract

Adulteration remains an issue in the dietary supplement industry, including botanical supplements. While it is common to employ a targeted analysis to detect known adulterants, this is difficult when little is known about the sample set. With this study, untargeted metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS) was employed to detect adulteration in botanical dietary supplements. A training set was prepared by combining Hydrastis canadensis L. with a known adulterant, Coptis chinensis Franch., in ratios ranging from 5 to 95% adulteration. The metabolomics datasets were analyzed using both unsupervised (principal component analysis and composite score) and supervised (SIMCA) techniques. Palmatine, a known H. canadensis metabolite, was quantified as a targeted analysis comparison. While the targeted analysis was the most sensitive method tested in detecting adulteration, statistical analyses of the untargeted metabolomics datasets detected adulteration of the goldenseal samples, with SIMCA providing the greatest discriminating potential.

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Availability of data and material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Original material also available upon request.

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Acknowledgments

The authors would like to thank our collaborator Dr. Olav M. Kvalheim (orcid.org/0000-0001-9432-8776) for his valuable assistance in data analysis and feedback for the semi-supervised analysis approach. Mass spectrometry analyses were conducted in the Triad Mass Spectrometry Facility at the University of North Carolina at Greensboro (https://chem.uncg.edu/triadmslab/).

Code availability

MzMine is an open-source software and readily available to the public. Sirius is created by Pattern Recognition Systems and can be purchased here: http://www.prs.no/Sirius/Sirius.html. The code for the composite score analysis is available here: https://github.com/jjkellogg/Composite-score.

Funding

Funding was provided by the National Institutes of Health National Center for Complementary and Integrative Health (NIH NCCIH), specifically the Center of Excellence for Natural Product Drug Interaction Research (NaPDI) [grant number U54AT008909] and a Ruth L. Kirschstein Postdoctoral National Research Service Award [grant number F32AT009816] to Joshua Kellogg.

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Author notes

  1. Joshua J. Kellogg

    Present address: Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, 16802, USA

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA

    E. Diane Wallace, Daniel A. Todd, Nadja B. Cech & Joshua J. Kellogg

  2. U.S. Department of Agriculture, Agricultural Research Service, Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Beltsville, MD, 20705, USA

    James M. Harnly

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  1. E. Diane Wallace
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Correspondence to Joshua J. Kellogg.

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Wallace, E.D., Todd, D.A., Harnly, J.M. et al. Identification of adulteration in botanical samples with untargeted metabolomics. Anal Bioanal Chem 412, 4273–4286 (2020). https://doi.org/10.1007/s00216-020-02678-6

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  • DOI: https://doi.org/10.1007/s00216-020-02678-6

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