scholarly journals Multiple Response Optimization of a HPLC Method for Analyzing Resorcinol and 4-n-Butyl Resorcinol in Lipid Nanoparticles

2021 ◽  
Vol 21 (2) ◽  
pp. 502
Author(s):  
Rini Dwiastuti ◽  
Dina Christin Ayuning Putri ◽  
Maywan Hariono ◽  
Florentinus Dika Octa Riswanto
Keyword(s):  
Quantitative Estimation ◽  
Lipid Nanoparticles ◽  
Hplc Method ◽  
Response Analysis ◽  
Multiple Response ◽  
Lipid Nanoparticle ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Nanoparticle Formulation ◽  
The Stability

Resorcinol and 4-n-butyl resorcinol have been used to improve skin health. However, these two compounds were unstable due to the oxidation process. Lipid nanoparticle formulation strategies were reported as the solution to overcome the stability problem for both resorcinol and 4-n-butyl resorcinol. Nevertheless, it is important to determine the content of resorcinol and 4-n-butyl resorcinol in lipid nanoparticle formulation. Aiming to develop the analytical method for resorcinol and 4-n-butyl resorcinol determination, a response surface methodology (RSM) was applied in the HPLC optimization stage. An optimized HPLC condition was obtained by generating a Box-Behnken design followed by multiple response analysis. It was obtained that optimized HPLC conditions due to the predictive multiple response optimization were methanol percentage of 50.0%, acetonitrile percentage of 18.1%, and flow rate of 0.6 mL min–1. This optimized condition was successfully applied and met the requirements of the system suitability test. Quantitative estimation was performed and resulted that the resorcinol and 4-n-butyl resorcinol content in lipid nanoparticles were 70.37 ± 0.47 and 95.07 ± 0.80 µg mL–1, respectively.

A Combinational Approach for Experts of Committee Machine in Wire Bonding Problem Solving

2013 ◽  
Vol 465-466 ◽  
pp. 751-755
Author(s):  
Seyed Jafar Golestaneh ◽  
Ismail Napsiah ◽  
Mohd Khairol Anuar Bin Mohd Ariffin ◽  
Say Hong Tang ◽  
Hassan Moslemi Naeini ◽  
...  
Keyword(s):  
Problem Solving ◽  
Experiment Design ◽  
Wire Bonding ◽  
Multiple Response ◽  
Committee Machine ◽  
Initial Approach ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Modelling And Optimization ◽  
Three Phases

There are three phases to solve multiple response optimization (MRO) problems. They are experiment design, modelling and optimization. Different committee machines (CMs) are applied for modeling of MRO problem. Two kinds of CM introduced by current authors in recent works. Initial approach includs utilization of all experts in CM and finally sequentilal combinational model (SCM) is created. Current study proposes a combinational approach to filter and select the best experts to create CM which yields optimum combinational model (OCM). Also , there was another modeling with name point approach model (PAM). This study investigated two different features of different SCMs, PAM and OCM in a famous MRO problem with name wire-bonding. This problem was defined in 1997 and then, several researchers solved it in next years. The results show PAM has minimum RMSE but its global desirability is low. Also overall RMSE has a slightly fall from SCMs to OCM , whereas, there is gently rise in global desirability from SCMs to OCM and consequently proposed approach or OCM provides superior results.

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