Differentiation between two "fang ji" herbal medicines, Stephania tetrandra and the nephrotoxic Aristolochia fangchi, using hyperspectral imaging. (February 2016) - Tankeu, Sidonie; Vermaak, Ilze; Chen, Weiyang; Sandasi, Maxleene; Viljoen, Alvaro
Graphical abstract: Aristolochic acid nephropathy can be prevented through the use of hyperspectral imaging in conjunction with chemometric data analysis to distinguish between Stephania tetrandra and Aristolochia fangchi . Highlights: Hyperspectral imaging can distinguish between the two different "fang ji" herbal medicines. The PLS-DA model accurately identified the species in an external dataset. The PLS-DA model accurately predicted the level of adulteration. This quality control method can prevent aristolochic acid nephropathy in consumers. Abstract: Stephania tetrandra ("hang fang ji") and Aristolochia fangchi ("guang fang ji") are two different plant species used in Traditional Chinese Medicine (TCM). Both are commonly referred to as "fang ji" and S. tetrandra is mistakenly substituted and adulterated with the nephrotoxic A. fangchi as they have several morphological similarities. A. fangchi contains aristolochic acid, a carcinogen that causes urothelial carcinoma as well as aristolochic acid nephropathy (AAN). In Belgium, 128 cases of AAN was reported while in China, a further 116 cases with end-stage renal disease were noted. Toxicity issues associated with species substitution and adulteration necessitate the development of reliable methods for the quality assessment of herbal medicines. Hyperspectral imaging in combination with partial least squares discriminant analysis (PLS-DA) is suggested as an effective method to distinguish between S. tetrandra and A. fangchi root powder. Hyperspectral images were obtained in the wavelength region of 920–2514 nm. Reduction of the dimensionality of the data was done by selecting the discrimination information range (964–1774 nm). A discrimination model with a coefficient of determination ( R 2 ) of 0.9 and a root mean square error of prediction (RMSEP) of 0.23 was created. The constructed model successfully identified A. fangchi and S. tetrandra samples inserted into the model as an external validation set. In addition, adulteration detection was investigated by preparing incremental adulteration mixtures of S. tetrandra with A. fangchi (10–90%) . Hyperspectral imaging showed the ability to accurately predict adulteration as low as 10%. It is evident that hyperspectral imaging has tremendous potential in the development of visual quality control methods which may prevent cases of aristolochic acid nephropathy in the future.