scholarly journals Quality Evaluation of Atractylodis Macrocephalae Rhizoma Based on Combinative Method of HPLC Fingerprint, Quantitative Analysis of Multi-Components and Chemical Pattern Recognition Analysis

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7124
Author(s):  
Cheng Zheng ◽  
Wenting Li ◽  
Yao Yao ◽  
Ying Zhou
Keyword(s):  
Pattern Recognition ◽  
High Performance ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Principal Component ◽  
Hplc Fingerprint ◽  
Atractylenolide I ◽  
Chemical Pattern ◽  
Pattern Recognition Analysis ◽  
Chemical Pattern Recognition

A method for the quality evaluation of Atractylodis Macrocephalae Rhizoma (AMR) based on high-performance liquid chromatography (HPLC) fingerprint, HPLC quantification, and chemical pattern recognition analysis was developed and validated. The fingerprint similarity of the 27 batches of AMR samples was 0.887–0.999, which indicates there was very limited variance between the batches. The 27 batches of samples were divided into two categories according to cluster analysis (CA) and principal component analysis (PCA). A total of six differential components of AMR were identified in the partial least-squares discriminant analysis (PLS-DA), among which atractylenolide I, II, III, and atractylone counted 0.003–0.045%, 0.006–0.023%, 0.001–0.058%, and 0.307–1.175%, respectively. The results indicate that the quality evaluation method could be used for quality control and authentication of AMR.

scholarly journals Quality Evaluation of Mahonia bealei (Fort.) Carr. Using Supercritical Fluid Chromatography with Chemical Pattern Recognition

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3684 ◽  
Author(s):  
Yang Huang ◽  
Zhengjin Jiang ◽  
Jue Wang ◽  
Guo Yin ◽  
Kun Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Supercritical Fluid ◽  
Supercritical Fluid Chromatography ◽  
Quality Evaluation ◽  
Predictive Ability ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Pattern ◽  
Mahonia Bealei ◽  
Chemical Pattern Recognition

Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (≥1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory. The established model demonstrated that the developed SFC coupled with PLS-DA method showed a great potential application for quality assessment of M. bealei.

Quality Assessment of Fructus xanthii Based on Fingerprinting Using High-Performance Liquid Chromatography

2012 ◽  
Vol 95 (4) ◽  
pp. 1053-1058 ◽  
Author(s):  
Liu Yang ◽  
Zhijun Su ◽  
Xing Zeng ◽  
Xiong Li ◽  
Zhifeng Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
High Performance ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Multicomponent System ◽  
Principal Component ◽  
Uv Spectra ◽  
Hplc Fingerprint ◽  
Hplc Profiles

Abstract Because almost every traditional Chinese medicine (TCM) is a multicomponent system, QC of TCMs always involves various difficulties. As a current popular quality assessment approach, focusing on qualitative and quantitative analysis of certain compounds contained in herbal medicine has been widely used for the sake of expediency rather than being a practical and realistic way. However, this method does not take the existence of other constituents into account. Comparatively, the chromatographic fingerprint of the components is a more suitable approach to holistically assess the quality of herbal drugs. Fructus xanthii is a well-known herbal drug listed in all editions of the Chinese Pharmacopoeia. However, there is no quality evaluation method given in its monograph, even for the above-mentioned expediency. This paper reports an HPLC fingerprinting method for quality evaluation of F. xanthii. The HPLC profiles of 27 batches of commercial samples were further analyzed using chemometric methods, including similarity evaluation and principal component analysis. As a result, the established HPLC fingerprint contained 23 characteristic peaks; therein, 13 peaks were unambiguously assigned by comparing their retention times and UV spectra with those of reference compounds, and five peaks were tentatively identified on the basis of their MS/MS fragmentation patterns and UV spectra. Moreover, it could be clearly observed that caffeoylquinic acid and its analogs predominate in F. xanthii. Except for three samples identified as outliers, 24 other commercial samples displayed similar HPLC profiles, indicating that the quality of the herbs from different markets is stable and consistent.

scholarly journals Application of UHPLC Fingerprints Combined with Chemical Pattern Recognition Analysis in the Differentiation of Six Rhodiola Species

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6855
Author(s):  
Didi Ma ◽  
Lijun Wang ◽  
Yibao Jin ◽  
Lifei Gu ◽  
Xiean Yu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rhodiola Rosea ◽  
Linear Discriminant ◽  
Chemical Pattern ◽  
Pattern Recognition Analysis ◽  
Chemical Pattern Recognition

Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.

scholarly journals Chemical Pattern Recognition for Quality Analysis of Lonicerae Japonicae Flos and Lonicerae Flos Based on Ultra-High Performance Liquid Chromatography and Anti-SARS-CoV2 Main Protease Activity

2022 ◽  
Vol 12 ◽  
Author(s):  
Lifei Gu ◽  
Xueqing Xie ◽  
Bing Wang ◽  
Yibao Jin ◽  
Lijun Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Quality Analysis ◽  
Bioactive Components ◽  
Recognition Model ◽  
Relationship Analysis ◽  
Chemical Pattern ◽  
Chemical Pattern Recognition

Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.

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