.png){kind=icon}
.png){kind=icon}
Return
Multi-Response Optimization Using Various Multi-Attribute Decision Making Methods and Regression Model in a Chromatographic Method
.png){kind=icon}
PRE
AI
DOI：10.1134/s1061934825602956.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High-performance liquid chromatography (HPLC) analysis is widely used in many analytical fields such as clinical, forensic, toxicology, environmental, and pharmaceutical analyses. The aim of this work is to develop an HPLC method for determining glimepiride in a supersaturatable self-nanoemulsifying formulation. This paper proposed an effective methodology for chromatographic multi-response optimization based on design of experiments and multi-attribute decision making. By applying the methodology, the optimal values of the HPLC factors were determined as 30% acetonitrile, pH 3.8, a flow rate of 0.9 mL/min, an injection volume of 12.9 mu L, a column temperature of 36.4 degrees C, and a buffer molarity of 13.6 mM, respectively. The influence analysis and optimization results of the chromatographic method were compared with the results of the Taguchi method. The results obtained from the former approach were well coincided with the results obtained from the latter method. This method is fast and easy to use, while giving satisfactory results under certain conditions. The proposed approach may be widely used to optimize process parameters in engineering practice.
Keywords:
high-performance liquid chromatography method
multi-attribute decision making (MADM)
multiple polynomial regression model
optimization
Journal
J
IF:
1.1
Papers:
146
Citations:
2.5K
Organization
No organization information available