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Improving the Quality of Response Surface Analysis of an Experiment for Coffee-Supplemented Milk Beverage: I. Data Screening at the Center Point and Maximum Possible R-Square

Authors
Rheem, SungsueOh, Sejong
Issue Date
2019
Publisher
KOREAN SOC FOOD SCIENCE ANIMAL RESOURCES
Keywords
response surface methodology; central composite design; center runs; outlier elimination; maximum possible R-square
Citation
FOOD SCIENCE OF ANIMAL RESOURCES, v.39, no.1, pp.114 - 120
Indexed
SCIE
SCOPUS
KCI
Journal Title
FOOD SCIENCE OF ANIMAL RESOURCES
Volume
39
Number
1
Start Page
114
End Page
120
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/68924
DOI
10.5851/kosfa.2019.e9
ISSN
2636-0772
Abstract
Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. As a design for a response surface experiment, a central composite design (CCD) with multiple runs at the center point is frequently used. However, sometimes there exist situations where some among the responses at the center point are outliers and these outliers are overlooked. Since the responses from center runs are those from the same experimental conditions, there should be no outliers at the center point. Outliers at the center point ruin statistical analysis. Thus, the responses at the center point need to be looked at, and if outliers are observed, they have to be examined. If the reasons for the outliers are not errors in measuring or typing, such outliers need to be deleted. If the outliers are due to such errors, they have to be corrected. Through a re-analysis of a dataset published in the Korean Journal for Food Science of Animal Resources, we have shown that outlier elimination resulted in the increase of the maximum possible R-square that the modeling of the data can obtain, which enables us to improve the quality of response surface analysis.
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