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      KCI등재 SCIE SCOPUS

      Optimizing Food Processing through a New Approach to Response Surface Methodology

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      https://www.riss.kr/link?id=A108489901

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      다국어 초록 (Multilingual 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 higherorder 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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      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...

      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 higherorder 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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      참고문헌 (Reference)

      1 SAS, "SAS/STAT user’s guide. Release 9.4"

      2 SAS, "SAS/GRAPH user’s guide. Release 9.4"

      3 Myers RH, "Response surface methodology: Process and product optimization using designed experiments" John Wiley & Sons 2009

      4 임성수 ; 오세종 ; 임인수, "Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources" 한국축산식품학회 37 (37): 139-146, 2017

      5 Oh S, "Optimizing conditions for the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-glucose medium by using response surface methodology" 61 : 3809-3814, 1995

      6 안성일 ; 김거유 ; 박준홍 ; 김재훈 ; 오덕근 ; 김무중 ; 주진우 ; 정동화, "Optimization of Manufacturing Conditions for Improving Storage Stability of Coffee-Supplemented Milk Beverage Using Response Surface Methodology" 한국축산식품학회 37 (37): 87-97, 2017

      7 Box GEP, "On the experimental attainment of optimum conditions" 13 : 1-38, 1951

      8 임성수 ; 임인수 ; 오세종, "Improving the Quality of Response Surface Analysis of an Experiment for Coffee-supplemented Milk Beverage: II. Heterogeneous Third-order Models and Multi-response Optimization" 한국축산식품학회 39 (39): 222-228, 2019

      9 임성수 ; 오세종, "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" 한국축산식품학회 39 (39): 114-120, 2019

      1 SAS, "SAS/STAT user’s guide. Release 9.4"

      2 SAS, "SAS/GRAPH user’s guide. Release 9.4"

      3 Myers RH, "Response surface methodology: Process and product optimization using designed experiments" John Wiley & Sons 2009

      4 임성수 ; 오세종 ; 임인수, "Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources" 한국축산식품학회 37 (37): 139-146, 2017

      5 Oh S, "Optimizing conditions for the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-glucose medium by using response surface methodology" 61 : 3809-3814, 1995

      6 안성일 ; 김거유 ; 박준홍 ; 김재훈 ; 오덕근 ; 김무중 ; 주진우 ; 정동화, "Optimization of Manufacturing Conditions for Improving Storage Stability of Coffee-Supplemented Milk Beverage Using Response Surface Methodology" 한국축산식품학회 37 (37): 87-97, 2017

      7 Box GEP, "On the experimental attainment of optimum conditions" 13 : 1-38, 1951

      8 임성수 ; 임인수 ; 오세종, "Improving the Quality of Response Surface Analysis of an Experiment for Coffee-supplemented Milk Beverage: II. Heterogeneous Third-order Models and Multi-response Optimization" 한국축산식품학회 39 (39): 222-228, 2019

      9 임성수 ; 오세종, "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" 한국축산식품학회 39 (39): 114-120, 2019

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