Open AccessResponse Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources
Author(s) -
Sungsue Rheem,
Insoo Rheem,
Sejong Oh
Publication year - 2017
Publication title -
han-gug chugsan sigpum hag-hoeji/korean journal for food science of animal resources
Language(s) - English
Resource type - Journals
eISSN - 2234-246X
pISSN - 1225-8563
DOI - 10.5851/kosfa.2017.37.1.139
Subject(s) - response surface methodology , computer science , set (abstract data type) , data set , polynomial and rational function modeling , order (exchange) , surface (topology) , polynomial , data mining , machine learning , artificial intelligence , mathematics , mathematical analysis , finance , economics , programming language , geometry
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 et al. (2014) published in the Korean Journal for Food Science of Animal Resources .