A Novel Approach in Herbal Quality Control Using Hyperspectral Imaging: Discriminating Between Sceletium tortuosum and Sceletium crassicaule
Publication Date
2013Author
Amukohe, Emmanuel Shikanga, Alvaro M Viljoen, Ilze Vermaak, Sandra Combrinck
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Show full item recordAbstract/ Overview
Sceletium tortuosum is the most sought after species of the genus Sceletium and is commonly included in
commercial products for the treatment of psychiatric conditions and neurodegenerative diseases. However, this species
exhibits several morphological and phytochemical similarities to S. crassicaule.
Objectives – The aim of this investigation was to use ultrahigh-performance liquid chromatography (UPLC) and hyperspectral
imaging, in combination with chemometrics, to distinguish between S. tortuosum and S. crassicaule, and to accurately predict
the identity of specimens of both species.
Methods – Chromatographic profiles of S. tortuosum and S. crassicaule specimens were obtained using UPLC with photodiode
array detection. A SisuChema near infrared hyperspectral imaging camera was used for acquiring images of the specimens
and the data was processed using chemometric computations.
Results – Chromatographic data for the specimens revealed that both species produce the psychoactive alkaloids that are
used as quality control biomarkers. Principal component analysis of the hyperspectral image of reference specimens for
the two species yielded two distinct clusters, the one representing S. tortuosum and the other representing S. crassicaule. A
partial least squares discriminant analysis model correctly predicted the identity of an external dataset consisting of
S. tortuosum or S. crassicaule samples with high accuracy (>94%).
Conclusions – A combination of hyperspectral imaging and chemometrics offers several advantages over conventional
chromatographic profiling when used to distinguish S. tortuosum from S. crassicaule. In addition, the constructed
chemometric model can reliably predict the identity of samples of both species from an external dataset. Copyright ©
2013 John Wiley & Sons, Ltd.
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- Department of Chemistry [337]