Identification of Salvia species using high-performance liquid chromatography combined with chemical pattern recognition analysis

2017 ◽  
Vol 41 (3) ◽  
pp. 609-617 ◽  
Author(s):  
Yang Wang ◽  
Kun Jiang ◽  
Lijun Wang ◽  
Dongqi Han ◽  
Guo Yin ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Chemical Pattern ◽  
Pattern Recognition Analysis ◽  
Chemical Pattern Recognition

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.

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 Application of Pattern Recognition Method for Color Assessment of Oriental Tobacco based on HPLC of Polyphenols

2003 ◽  
Vol 20 (5) ◽  
pp. 356-359 ◽  
Author(s):  
S Dagnon ◽  
A Edreva
Keyword(s):  
Pattern Recognition ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Chlorogenic Acid ◽  
High Performance ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Additional Tool ◽  
Hplc Profiles

AbstractThe color of Oriental tobaccos was organoleptically assayed, and high performance liquid chromatography (HPLC) of polyphenols was performed. The major tobacco polyphenols (chlorogenic acid, its isomers, and rutin), as well as scopoletin and kaempferol-3-rutinoside were quantified. HPLC polyphenol profiles were processed by pattern recognition method (PRM), and the values of indexes of similarity (Is,%) between the cultivars studied were determined. It was shown that data from organoleptic color assessment and from PRM based on HPLC profiles of polyphenols of the cultivars studied are largely compatible. Hence, PRM can be suggested as an additional tool for objective color evaluation and classification of Oriental tobacco.

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