Quality evaluation of Astragali Radix through chemical pattern recognition of fingerprint by HPLC-DAD-ELSD

2013 ◽  
Keyword(s):  
Pattern Recognition ◽  
Quality Evaluation ◽  
Astragali Radix ◽  
Chemical Pattern ◽  
Chemical Pattern Recognition

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.

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.

Complex chemical pattern recognition with sensor array: the discrimination of vintage years of wine

1995 ◽  
Vol 25 (1-3) ◽  
pp. 801-804 ◽  
Author(s):  
Corrado Di Natale ◽  
Fabrizio A.M. Davide ◽  
Arnaldo D'Amico ◽  
Giorgio Sberveglieri ◽  
Paulo Nelli ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Sensor Array ◽  
Complex Chemical ◽  
Chemical Pattern ◽  
Chemical Pattern Recognition
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