Indentification And Assessment Of Chemical Fingerprint Chromatography In Prais Polyphylla Var. Yunnanensis

The traditional Chinese medicine?TCM?with good quality is the premise to ensure the safety and effectiveness in clinical efficacy,meanwhile quality control and assessment is the main means to ensure the quality of TCM.Recently,the quality evaluation of TCM is research hotspot.As we all know,there are several factors which could affect the quality of herbal medicines significantly,such as harvest time,geographical origin,processing and so on.Therefore,it is important to find a scientific,effective,comprehensive and practical method to control and assess the quality of TCM.Prais polyphylla Smith var.yunnanensis?Franch.?Hand.Mazz.is a perennial herb,which belongs to family of Liliaceae.This crude herb has been used in traditional Chinese medicine for treatment of fractures,parotitis,abscess,epilepsy and so on.In this research,Fourier transform infrared?FT-IR?spectroscopy,high performance liquid chromatography?HPLC?and ultra-performance liquid chromatography tandem mass spectrometry?UPLC-MS/MS?combined with chemometrics were applied to qualitative identification and qualitative assessment of P.polyphylla var.yunnanensis with different other Liliaceae species,different harvest time and different processed.The results provided theoretical foundation and practical value for exploitation and utilization of P.polyphylla var.yunnanensis.The dissertation is composed of five sections.Section 1 is about genetic relationship of P.polyphylla var.yunnanensis and its wild relatives.Section 2 is about the development of method for rapid prediction of the content of polyphyllin in various species of Paris.Section 3 is about qualitative identification and qualitative assessment of P.polyphylla var.yunnanensis harvested in different years.The comparative analysis of different processed P.polyphylla var.yunnanensis have been reported in section 4.Section 5 is a review about progress in application of infrared spectroscopy to identification and quality evaluation of Chinses medicine.?1?The infrared spectrum information of fifty samples of P polyphylla var.yunnanensis,Paris polyphylla var.alba,Paris mairei,Paris vietnamensis and Paris axialis var.axialis was collected.The original infrared spectra data were pretreated by automatic baseline correction,automatic smoothing,ordinate normalization,multiplicative scatter correction and second derivative,and analyzed by principal component analysis?PCA?,partial least squares discriminant analysis?PLS-DA?and hierarchical cluster analysis?HCA?.The common peaks of 1 653,1 156,1 082,1 021,925,851,759,572 and 524 cm^-1 in the original spectra data of fifty samples might be relative to the content of flavonoids,starches and glycosides.The absorption peaks of1 535 and 1 369 cm^-1 belonged to P mairei and P axialis var.axialis,respectively which could be distinguished from other three species.By PCA and PLS-DA,the former one which can accurately distinguish five species of wild genus Paris presented a better classification result than the latter.HCA and vector included angle cosine analysis can reflect the genetic relationship of P polyphylla var.yunnanensis and its wild relatives.P polyphylla var.alba and P vietnamensis had closed relationship with P polyphylla var.Yunnanensis while P mairei and P axialis var.axialis were relative far.?2?In order to evaluate the quality of Paris rapidly and ensure the efficacy in clinical therapy,we quantified the contents of polyphyllin?,polyphyllin?and polyphyllin?using FT-IR spectroscopy with PLSR.The method of evaluating the quality of Paris was established.Infrared spectra of 78 samples from various species in different origins were collected.The contents of polyphyllin?,polyphyllin?and polyphyllin?were determined by HPLC.The HPLC data were combined with the spectral data to predict the contents of three polyphyllin rapidly.Multiplicative signal correction?MSC?,standard normal variate?SNV?,orthogonal signal correction?OSC?,first derivative?1st Der?and second derivative?2nd Der?were utilized for the spectral preprocessing.Then,the optimized spectral data were used to establish the quantitative prediction model based on PLSR.The results showed that the best spectral pretreatment of polyphyllin?and?were MSC+OSC+2nd Der and that of polyphyllin?was MSC+SNV+OSC+2nd Der.In the quantitative calibration model,the determination coefficients?R²?of polyphyllin?,polyphyllin?and polyphyllin?were 0.930 8,0.934 8 and 0.912 3,respectively while the Root mean square error of estimation?RMSEE?were 1.855 0,0.632 3 and 0.001 6,respectively.In the verification model,the R² of polyphyllin?,polyphyllin?and polyphyllin?were0.948 8,0.703 6 and 0.801 7,respectively as well as the root mean square error of prediction?RMSEP?were 1.704 6,1.227 8 and 0.002 0,respectively.Because of the predictive value of quantitative model was closed to the real value,the effect of the model was good.The model of polyphyllin?was better than that of polyphyllin?and polyphyllin?.?3?In this study,FT-IR spectroscopy coupled with PCA and PLSR was applied to discriminate samples of P.polyphylla var.yunnanensis harvested in different years and determine the content of polyphyllin I,II,VI,and VII in P.polyphylla var.yunnanensis.Meanwhile,UPLC-MS/MS was used to study the dynamic changes of P.polyphylla var.yunnanensis harvested in different years?4,5,7,8,9,12,and 13 years old?.According to the UPLC-MS/MS result,the optimum harvest time of P.polyphylla var.yunnanensis is 8 years,due to the highest yield of four active components.By the PCA model,P.polyphylla var.yunnanensis could be exactly discriminated,except that two 8-year-old samples were misclassified as 9-year-old samples.For the prediction of polyphyllin I,II,VI,and VII,the quantitative results are satisfactory,with a high value for the R² and low values for RMSEE,root-mean-square error of cross-validation?RMSECV?and RMSEP.?4?In this paper,P.polyphylla var.yunnanensis which were processed by nine different methods were conducted comparative analysis by infrared spectroscopy combined with chemometrics,and the principal component analysis-Mahalanobis distance?PCA-MD?discriminant model was established to differentiate them.The original infrared spectra data were preprocessed by automatic baseline correction and ordinate normalization,and the averaged spectra were obtained.The averaged and second derivative spectra showed that:?1?The main characteristic absorption peaks were 3 387,2 923,1 745,1 463,1 338,1 240,1 207,1 158,1 180,1 080,1 048,1020,988,921,895,859,833,765,708,572 and 529 cm^-1.?2?The peak shape of samples was almost alike,which could exhibit the infrared spectral features of processed P.yunnanensis.?3?Some differences of a few characteristic absorption peaks existed in number,position and absorption intensity,which indicated that the chemical components and content were changed after different processing.The infrared spectra data were pretreated by MSC,SNV,1st Der,2nd Der and Savitzky-Golay?SG?smoothing.Samples were divided into calibration set and prediction set at the ratio of 3:1 by Kennard-Stone algorithm.Then,the optimized spectra data were used to establish the discrimination model based on PCA-MD.The results showed that the best spectral pretreatment of PCA-MD model was 1st Der+SG?11:3?.The cumulative accounting was 88.2%,when extracted the first five principal components.The first three principal components were selected for establishing the3D scattered plot of PCA-DA model.It is obvious that samples with different processed methods could be grouped completely.The clustering result of P.yunnanensis I,H,G and F were better than others,and the first three?I,H and G?were nearer.It indicated that the chemical composition of processing by sun-drying and oven drying were similar to traditional processing method.Additionally,P.yunnanensis D was close to P.yunnanensis E,it conjectured that chemical compositions of processing by microwave drying and steam treatment were similar.The prediction set could accurately conform to the calibration set,and the accuracy of PCA-MD model was 100%.