Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: A quantitative structure-retention relationship (QSRR) Approach

International Journal of Molecular Sciences

Volumn 13, Issue 11, 2012, Pages 15387-15400

  Akbar, Jamshed ^a   Iqbal, Shahid ^a   Batool, Fozia ^a   Karim, Abdul ^a   Chan, Kim Wei ^b  

^a UNIVERSITY OF SARGODHA   (Pakistan)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Artificial neural networks; Naturally occurring phenolic compounds; QSRR (quantitative structure retention relationship); Reversed phase liquid chromatography; Unsupervised forward selection

Indexed keywords

PHENOL DERIVATIVE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR MODEL; MULTIPLE LINEAR REGRESSION ANALYSIS; PROCESS OPTIMIZATION; QUANTITATIVE STRUCTURE RETENTION RELATION; QUANTUM MECHANICS; REVERSED PHASE LIQUID CHROMATOGRAPHY; STEP WISE MULTIPLE REGRESSION; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; VALIDATION PROCESS; CHEMICAL MODEL; CHEMISTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; THEORETICAL MODEL;

CHROMATOGRAPHY, REVERSE-PHASE; MODELS, CHEMICAL; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PHENOLS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84870708068     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms131115387     Document Type: Article

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