ArticleFood science of animal resources2023
Optimizing Food Processing through a New Approach to Response Surface Methodology.
Article in Food science of animal resources, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Screening to Optimization: Strategic Implementation of Design of Experiments (DOE) for Robust Nanoparticle Formulation.Polymers · 2026Review
- Ultrasonic-Assisted Natural Deep Eutectic Solvent Extraction of Proanthocyanidins fromMolecules (Basel, Switzerland) · 2026Article
- Modeling and Optimization ofFoods (Basel, Switzerland) · 2026Article
- Green Coconut Biorefinery: RSM and ANN-GA Optimization of Coconut Water Microfiltration with IntegratedTechno-Economic Analysis.Foods (Basel, Switzerland) · 2026Article
- Modelling the Combined Effects of Oxalic Acid, Water Activity, and pH on the Growth and Mycotoxin Production ofFoods (Basel, Switzerland) · 2025Article
- Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast.Foods (Basel, Switzerland) · 2025Article
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Abstract
In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.
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