Chromatographic separation techniques and data handling methods for herbal fingerprints: A review

2011 ◽  
Vol 690 (2) ◽  
pp. 148-161 ◽  
Author(s):  
Christophe Tistaert ◽  
Bieke Dejaegher ◽  
Yvan Vander Heyden
Keyword(s):  
Chromatographic Separation ◽  
Data Handling ◽  
Separation Techniques ◽  
Handling Methods

scholarly journals PHYTOCHEMICAL SCREENING AND BIOLOGICAL EVALUATION OF DYPSIS LEPTOCHEILOS LEAVES EXTRACT AND MOLECULAR DOCKING STUDY OF THE ISOLATED COMPOUNDS

2020 ◽  
pp. 106-113
Author(s):  
HAITHAM ALI IBRAHIM ◽  
FATEHIA SAYED ELSHARAWY ◽  
MAHMMOUD ELHASSAB ◽  
SAMAH SHABANA ◽  
EMAN GABER HAGGAG
Keyword(s):  
Antioxidant Activity ◽  
Phenolic Compounds ◽  
Ethyl Acetate ◽  
Chromatographic Separation ◽  
Ethyl Acetate Fraction ◽  
Docking Study ◽  
Separation Techniques ◽  
Reference Drug ◽  
First Time ◽  
Mcf 7

Objective: phytochemical investigation of the ethyl acetate fraction (EAF) of 80% aqueous methanol extract (AME) of Dypsis leptocheilos leaves, in addition to evaluation of the antioxidant, cytotoxic and antimicrobial activities of the AME and EAF. Docking was used to predict and understand cytotoxicity of the isolated compounds. Methods: The ethyl acetate fraction (EAF) of Dypsis leptocheilos leaves was subjected to different chromatographic separation techniques. Structures of the isolated compounds were established by different spectroscopic techniques (1H/13C NMR). Antioxidant activity was evaluated by DPPH assay, while cytotoxicity was evaluated by MTT cell viability assay. Antimicrobial activity was evaluated by agar diffusion method. The docking study was conducted using Auto Dock Vina; the estrogen receptor (PDB 5t92) was used as a receptor for the docking. Results: Chromatographic separation techniques were led to the isolation of five phenolic compounds; these compounds were identified to be apigenin 8-C-β-D-glucopyranoside (Vitexin) (1), apigenin 6-C-β-D-glucopyranoside (Isovitexin) (2), luteolin 7-O-β-D-glucopyranoside (3), luteolin 8-C-β-D-glucopyranoside (Orientin) (4), luteolin 6-C-β-D-glucopyranoside (Isoorientin) (5). They were isolated and identified for the first time from this plant species. The AME and EAF showed moderate activity against Gram positive and Gram negatvie bacteria, while both of them showed similar and powerful antioxidant activity with SC50 = 12.8±0.56 µg/ml and SC50 = 17±0.77 µg/ml respectively, compared to ascorbic (reference drug) SC50 = 14.2±0.35 µg/ml. The EAF showed higher cytotoxic activity on the MCF-7 cells (human breast cancer cell line), with IC50 = 12.3 ± 1.82 µg/ml, compared to Vinblastine Sulfate (reference drug). All isolated compounds showed good binding affinity to the estrogen receptors existed in the MCF-7 cell. Conclusion: Five phenolic compounds were isolated for the first time from the EAF of Dypsis leptocheilos leaves. The AME and EAF extracts showed variable antioxidant, antimicrobial and cytotoxic activities.

Missing Data Handling Methods

2018 ◽  
pp. 139-185 ◽  
Author(s):  
Craig K. Enders ◽  
Amanda N. Baraldi
Keyword(s):  
Missing Data ◽  
Data Handling ◽  
Handling Methods

Fast chromatographic separation techniques as competitors to chemical and biochemical sensor systems

1994 ◽  
Vol 349 (5) ◽  
pp. 338-345 ◽  
Author(s):  
Karl Cammann ◽  
Wolfgang Kleib�hmer ◽  
Elmar Mussenbrock ◽  
Bernd Ro� ◽  
Frank Zuther
Keyword(s):  
Chromatographic Separation ◽  
Sensor Systems ◽  
Separation Techniques ◽  
Biochemical Sensor

Comparison of partial least square algorithms in hierarchical latent variable model with missing data

SIMULATION ◽  
2020 ◽  
Vol 96 (10) ◽  
pp. 825-839
Author(s):  
Hao Cheng
Keyword(s):  
Missing Data ◽  
Quantile Regression ◽  
Latent Variables ◽  
Latent Variable ◽  
Partial Least Square ◽  
Least Square ◽  
Latent Variable Model ◽  
Data Handling ◽  
Variable Model ◽  
Handling Methods

Missing data is almost inevitable for various reasons in many applications. For hierarchical latent variable models, there usually exist two kinds of missing data problems. One is manifest variables with incomplete observations, the other is latent variables which cannot be observed directly. Missing data in manifest variables can be handled by different methods. For latent variables, there exist several kinds of partial least square (PLS) algorithms which have been widely used to estimate the value of latent variables. In this paper, we not only combine traditional linear regression type PLS algorithms with missing data handling methods, but also introduce quantile regression to improve the performances of PLS algorithms when the relationships among manifest and latent variables are not fixed according to the explored quantile of interest. Thus, we can get the overall view of variables’ relationships at different levels. The main challenges lie in how to introduce quantile regression in PLS algorithms correctly and how well the PLS algorithms perform when missing manifest variables occur. By simulation studies, we compare all the PLS algorithms with missing data handling methods in different settings, and finally build a business sophistication hierarchical latent variable model based on real data.

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