A multi-detector chromatographic approach for characterization and quantitation of botanical constituents to enable in silico safety assessments View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-08

AUTHORS

Timothy R. Baker, Brian T. Regg

ABSTRACT

An approach has been developed to characterize the individual chemical constituents of botanicals. The challenge was to identify and quantitate the significant analytes in these complex mixtures, largely in the absence of authentic standards. The data-rich information content generated by this three-detector configuration was specifically intended to be used to conduct safety and/or quality evaluations for complex botanical mixtures, on a chemical constituent basis. The approach utilized a broad gradient UHPLC chromatographic separation. Following the chromatographic separation and UV detection, the eluent was split and sent into a charged aerosol detector (CAD), for quantitation, and a quadrupole/time-of-flight high-resolution mass spectrometer for component identification. The known bias of the otherwise universal CAD response, for organic solvent composition of the mobile phase, was compensated by the addition of an inverse gradient make-up stream. This approach and the orthogonal information content from the chromatography and three different detectors was specifically designed to enable in-silico safety assessments. These guide, minimize, or even eliminate the need for in vivo and in vitro safety assessments. The methodology was developed and demonstrated using standardized extracts of Ginkgo biloba. Results from the development of this novel approach and the characterization example reported here demonstrate the suitability of this instrumental configuration for enabling in-silico safety assessments and proving general quality assessments of botanicals. More... »

PAGES

5143-5154

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-018-1163-y

DOI

http://dx.doi.org/10.1007/s00216-018-1163-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105480895

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29995187


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