Combining Chemometrics and the Technique for the Order of Preference by Similarity to Ideal Solution: A New Approach to Multiple-Response Optimization of Hplc Compared to Desirability Function Publisher



Mousavi L¹ ; Tamiji Z^{1, 2} ; Hajimahmoodi M¹ ; Amini M³ ; Ahmadkhaniha R⁴ ; Asadi M⁵ ; Khoshayand MR^{1, 2} Show Affiliations
Source: Microchemical Journal Published:2020

Abstract

Some devices are used in the pharmaceutical industry to produce and control the quality of a variety of pharmaceutical products; therefore, it would be helpful to identify the factors affecting the performance of the devices and optimize them in terms of time and cost. In this regard, the use of experimental design approaches to optimization is important as it can help a device achieve its highest efficiency. One of the most popular devices in the pharmaceutical industry is high-performance liquid chromatography (HPLC). In view of its wide use in the industry and the number of factors affecting it, it is necessary to design and validate a new method that can simultaneously optimize its different parameters. In this study, the method of the simultaneous optimization of the parameters was implemented using the Box-Behnken design and multiple-criteria decision-making (MCDM) by Derringer's desirability function and technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) method. In this regard, mobile phase ratio, flow rate, and pH were simultaneously changed, and capacity factor, total analysis time, and resolution were considered as responses. To validate the method, the key parameters of the method were determined and used for all the validation steps. Therefore, this approach to optimizing HPLC factors is new and very cost-effective with diverse applications in the pharmaceutical industry. © 2020 Elsevier B.V.