Phytochemistry. 2016 Feb;122:213-22. doi: 10.1016/j.phytochem.2015.11.008. Epub 2015 Nov 26.
- 1Department
of Pharmaceutical Sciences, Faculty of Science, Tshwane University of
Technology, Private Bag X680, Pretoria 0001, South Africa.
- 2Department
of Pharmaceutical Sciences, Faculty of Science, Tshwane University of
Technology, Private Bag X680, Pretoria 0001, South Africa; SAMRC Herbal
Drugs Research Unit, Faculty of Science, Tshwane University of
Technology, Private Bag X680, Pretoria 0001, South Africa. Electronic
address: vermaaki@tut.ac.za.
- 3Department of
Pharmaceutical Sciences, Faculty of Science, Tshwane University of
Technology, Private Bag X680, Pretoria 0001, South Africa; SAMRC Herbal
Drugs Research Unit, Faculty of Science, Tshwane University of
Technology, Private Bag X680, Pretoria 0001, South Africa.
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
920-2514nm. Reduction of the dimensionality of the data was done by
selecting the discrimination information range (964-1774nm). 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.
Copyright © 2015 Elsevier Ltd. All rights reserved.
KEYWORDS:
Aristolochia
fangchi; Aristolochiaceae; Chemometrics; Hyperspectral imaging;
Menispermaceae; Partial least squares discriminant analysis; Quality
control; Stephania tetrandra; Toxicity
