A solid-phase extraction (SPE) cleanup and a liquid chromatographic (LC) method with UV detection is presented for analysis of up to 7 ephedrine alkaloids in herbal products. Alkaloids from herbal products are extracted with acidified buffer, isolated on a propylsulfonic acid SPE column, eluted with a high-ionic-strength buffer, and separated by LC with detection at 255 nm. LC separation is performed by isocratic elution on a YMC phenyl column with 0.1 M sodium acetate-acetic acid (pH = 4.8) containing triethyl-amine and 2% acetonitrile. Ephedrine alkaloids are completely separated in 15 min. Average recovery of 5 common alkaloids from 3 spiked matrixes is 90%, with an average relative standard deviation (RSD) of 4.4% for alkaloid spikes between 0.5 and 16 mg/g. Average quantitation of ephedrine and pseudoephedrine from 6 herbal products is 97% of declared label claims, and average quantitation of synephrine from an herbal dietary product is 85% of label claim (RSD, 3.2%). Recoveries of synephrine, norephedrine, ephedrine, pseudoephedrine, N-methylephedrine, and N-methylpseudoephedrine spiked in 4 herbal products averaged 95%. Results of ruggedness testing and of a second laboratory validation of the procedure are also presented.
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Front Pharmacol
January 2025
Department of Pathology, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.
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Intercollege Graduate Degree Program in Plant Biology, Pennsylvania State University, University Park, PA, USA.
Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products.
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