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Central composite design with the help of multivariate curve resolution in loadability optimization of RP‐HPLC to scale‐up a binary mixture
Author(s) -
Taheri Mohammadreza,
MoazeniPourasil Roudabeh Sadat,
SheikhOliaLavasani Majid,
Karami Ahmad,
Ghassempour Alireza
Publication year - 2016
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201500526
Subject(s) - multivariate statistics , elution , chromatography , resolution (logic) , binary number , central composite design , design of experiments , software , quality by design , high performance liquid chromatography , process (computing) , computer science , process engineering , chemistry , mathematics , machine learning , artificial intelligence , statistics , response surface methodology , engineering , arithmetic , particle size , programming language , operating system
Chromatographic method development for preparative targets is a time‐consuming and subjective process. This can be particularly problematic because of the use of valuable samples for isolation and the large consumption of solvents in preparative scale. These processes could be improved by using statistical computations to save time, solvent and experimental efforts. Thus, contributed by ESI‐MS, after applying DryLab software to gain an overview of the most effective parameters in separation of synthesized celecoxib and its co‐eluted compounds, design of experiment software that relies on multivariate modeling as a chemometric approach was used to predict the optimized touching‐band overloading conditions by objective functions according to the relationship between selectivity and stationary phase properties. The loadability of the method was investigated on the analytical and semi‐preparative scales, and the performance of this chemometric approach was approved by peak shapes beside recovery and purity of products.