Improving the Quality of Response Surface Analysis of an Experiment for Coffee-supplemented Milk Beverage: II. Heterogeneous Third-order Models and Multi-response Optimization

• 발행기관: 한국축산식품학회
• 저자명: Sungsue Rheem Insoo Rheem Sejong Oh
• 간행물 정보: 『Food Science of Animal Resources』Food Science of Animal Resources 제39권 제2호, 222~228쪽, 전체 7쪽
• 주제분류: 농수해양 > 식품과학
• 파일형태: PDF
• 발행일자: 2019.04.30

영문 초록

This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are Y1=particle size and Y2=zeta-potential, two factors are F1=speed of primary homogenization (rpm) and F2=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize Y1 and maximize Y2. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is (F1, F2)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.

목차

Introduction

Materials and Methods

Results and Discussion

Conclusion

키워드

response surface methodology central composite design heterogeneous thirdorder model multi-response optimization desirability