Modified secured principal component regression for detection of unexpected chromatographic features in herbal fingerprints.
Secured principal component regression is modified for the qualitative analysis of chromatographic fingerprint data sets of herbal samples with residual concentrations. After chromatographic shift-correction and autoscaling are performed on the data, this modified secured principal component regression (msPCR) can detect unexpected chromatographic features in various herbal fingerprints. The successful application of msPCR to two real herbal medicines of Erigeron breviscapus from different geographical origins and Ginkgo biloba from various sources or vendors demonstrates that the proposed method can detect reasonably unexpected features differing from the regulars or not being modeled. From a chemical point of view, the causes have also been explained to corroborate the results. Moreover, it presents a viable approach for the qualitative evaluation of diverse herbal objects with a regular class of chromatographic fingerprints.
Li
Bo-Yan
BY
College of Chemistry and Chemical Engineering, Research Center of Modernization of Chinese Herbal Medicines, Central South University, Changsha, 410083, P. R. China.
Hu
Yun
Y
Liang
Yi-Zeng
YZ
Xie
Pei-Shan
PS
Ozaki
Yukihiro
Y