What question did this study set out to answer?

This work aims to develop an effective HPLC method for determining glimepiride in a specific formulation.

June 3, 2026Journal of Analytical Chemistry0 citations

Multi-Response Optimization Using Various Multi-Attribute Decision Making Methods and Regression Model in a Chromatographic Method

JKJu-Song KimKim Chaek University of Technology WRWon-Gil RiKim Chaek University of Technology MKMyong-Ho KyeKim Chaek University of Technology

Puntos clave

This work aims to develop an effective HPLC method for determining glimepiride in a specific formulation.
Developed a chromatographic method focusing on multi-response optimization using HPLC.
Applied design of experiments and various multi-attribute decision making methods.
Determined optimal HPLC factors including acetonitrile percentage, pH, flow rate, injection volume, temperature, and molarity.
Optimal HPLC factors identified were 30% acetonitrile, pH 3.8, flow rate of 0.9 mL/min, injection volume of 12.9 μL, column temperature of 36.4°C, and buffer molarity of 13.6 mM.
Results from the proposed method were consistent with those obtained using the Taguchi method, indicating accuracy and reliability.
The methodology is fast, easy to implement, and could be widely applicable for optimizing process parameters.

Resumen

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 μL, a column temperature of 36.4°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.

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