TY - JOUR
T1 - Differentiating Puerariae Lobatae Radix and Puerariae Thomsonii Radix using HPTLC coupled with multivariate classification analyses
AU - Wong, Ka H.
AU - Razmovski-Naumovski, Valentina
AU - Li, Kong M.
AU - Li, George Q.
AU - Chan, Kelvin
PY - 2014
Y1 - 2014
N2 - Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR.
AB - Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR.
UR - http://handle.uws.edu.au:8081/1959.7/547333
U2 - 10.1016/j.jpba.2014.02.007
DO - 10.1016/j.jpba.2014.02.007
M3 - Article
SN - 0731-7085
VL - 95
SP - 11
EP - 19
JO - Journal of Pharmaceutical and Biomedical Analysis
JF - Journal of Pharmaceutical and Biomedical Analysis
ER -