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Sandasi, M., Vermaak, I., Chen, W., Viljoen, A.M. 2014. Skullcap and germander: Preventing potential toxicity through the application of hyperspectral imaging and multivariate image analysis as a novel quality control method. Planta Medica 80: 1329-1339.
Scutellaria lateriflora L. (Lamiaceae) (skullcap) is a medicinal herb that has a long history
of use in the treatment of ailments such as insomnia and anxiety. Commercial
herbal formulations claiming to contain S. laterifolia herba have flooded the consumer markets. However, due
to intentional or unintentional adulteration, cases of hepatotoxicity have been reported. Possible adulteration with the
potentially hepatotoxic Teucrium spp., T. canadense L.(Lamiaceae) and T. chamaedrys L. has been reported. In this study,
hyperspectral imaging in combination with multivariate image analysis methods
was used to differentiate S. laterifolia,T. canadense and T. chamaedrys raw materials in a non-destructive manner.
Furthermore, the ability to detect adulteration of raw materials using the
developed multivariate models was also investigated. Chemical images were
captured using a shortwave infrared (SWIR) pushbroom imaging system in the wavelength
range 920–2514 nm. Principal component analysis (PCA) was applied to the images
to investigate chemical differences between the species. Partial least squares
discriminant analysis was used to model pre-assigned class images and the
classification model predicted the levels of adulteration in spiked raw
materials. UHPLC-MS as an independent analytical technique was used to confirm
chemical differences between the three species. The ability of hyperspectral
chemical imaging as a non-destructive technique in the differentiation of the
three species was achieved with three distinct clusters in the score scatter
plot. A 92.3% variation in modelled data using PC1 and PC2 was correlated to
chemical differences between the three species. Near infrared (NIR) signals in
the region 1924 nm and
2092 nm (positive P1), 1993 nm and 2186 nm (negative P1), 1918 nm, 2092 nm and 2266 nm (positive P2), as well as 1993 nm and 2303 nm
(negative P2) were identified as containing discriminating
information using the loadings line plots. Chemical imaging of spiked samples showed
spatial orientation of contaminants within the powdered samples and percentage
adulteration was accurately predicted at levels > 40% adulteration based
on pixel abundance.