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    A Novel Approach in Herbal Quality Control Using Hyperspectral Imaging: Discriminating Between Sceletium tortuosum and Sceletium crassicaule

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    Phytochemical Analysis - 2013 - Shikanga - A Novel Approach in Herbal Quality Control Using Hyperspectral Imaging (1).pdf (846.8Kb)
    Publication Date
    2013
    Author
    Emmanuel Amukohe Shikanga, a Alvaro M. Viljoen,b * Ilze Vermaakb and Sandra Combrincka
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    Abstract/Overview
    ntroduction–Sceletium tortuosumis the most sought after species of the genusSceletiumand is commonly included incommercial products for the treatment of psychiatric conditions and neurodegenerative diseases. However, this speciesexhibits several morphological and phytochemical similarities toS. crassicaule.Objectives–The aim of this investigation was to use ultrahigh-performance liquid chromatography (UPLC) and hyperspectralimaging, in combination with chemometrics, to distinguish betweenS. tortuosumandS. crassicaule, and to accurately predictthe identity of specimens of both species.Methods–Chromatographic profiles ofS. tortuosumandS. crassicaulespecimens were obtained using UPLC with photodiodearray detection. A SisuChema near infrared hyperspectral imaging camera was used for acquiring images of the specimensand the data was processed using chemometric computations.Results–Chromatographic data for the specimens revealed that both species produce the psychoactive alkaloids that areused as quality control biomarkers. Principal component analysis of the hyperspectral image of reference specimens forthe two species yielded two distinct clusters, the one representingS. tortuosumand the other representingS. crassicaule.Apartial least squares discriminant analysis model correctly predicted the identity of an external dataset consisting ofS. tortuosumorS. crassicaulesamples with high accuracy (>94%).Conclusions–A combination of hyperspectral imaging and chemometrics offers several advantages over conventionalchromatographic profiling when used to distinguishS. tortuosumfromS. crassicaule. In addition, the constructedchemometric 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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    https://repository.maseno.ac.ke/handle/123456789/5486
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