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KMID : 1024620230430020374
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Food Science of Animal Resources
2023 Volume.43 No. 2 p.374 ~ p.381
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Optimizing Food Processing through a New Approach to Response Surface Methodology
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Rheem Sung-Sue
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Abstract
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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 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 higher-order 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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KEYWORD
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response surface methodology, lack of fit, three-step modeling, balanced higher-order model, balanced highest-order model
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