ArticleKorean journal for food science of animal resources2017
Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the
Article in Korean journal for food science of animal resources, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimization of pressure-assisted enzymatic treatment for fiber solubilization and structural modification of Korean cabbage by-products.Food chemistry: X · 2026Article
- Modeling and Optimization of Safflower Seed (Foods (Basel, Switzerland) · 2026Article
- Integrating design-of-experiments (DOE) optimization and risk assessment towards a safe and simplified electroporation protocol for Toxoplasma gondii.PLoS neglected tropical diseases · 2026Article
- Maximizing diesel removal from contaminated sand usingHeliyon · 2023Article
- Revealing the Sequence Characteristics and Molecular Mechanisms of ACE Inhibitory Peptides by Comprehensive Characterization of 160,000 Tetrapeptides.Foods (Basel, Switzerland) · 2023Article
- Optimizing Food Processing through a New Approach to Response Surface Methodology.Food science of animal resources · 2023Article
- Multiple response optimizations on the leached-spray-dried bancha green tea towards healthy ageing.Scientific reports · 2022Article
- Optimization of Fermentation Conditions ofFoods (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park
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Registered trials
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