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A novel approach in herbal quality control using hyperspectral imaging: discriminating between Sceletium tortuosum
Emmanuel Amukohe Shikanga1, Alvaro M Viljoen, Ilze Vermaak
1Department of Chemistry, Tshwane University of Technology, Private Bag X680, Pretoria, 0001, South Africa.
Introduction:
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.
