• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.97-108
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• 2018

It has been routine practice in regression analysis to check the validity of the assumed model by the use of regression diagnostics tools. Outliers and influential observations often distort the regression output in an undesired manner. Jang and Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014) proposed a graphical method (called a firework plot) so that there could be an exploratory visualization of the trace of the impact of the possible outliers and influential observations on individual regression coefficients and the overall residual sum of the squares measure. This paper further extends a graphical approach to a multi-response surface methodology problem.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.165-173
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• 2001

The desirability function approach by Derringer and Suich (1980) and the generalized distance approach by Khuri and Conlon (1981) are two major approaches to multiresponse optimization for improvement of quality of a product or process. So far, the desirability function method has been the only tool for multiresponse optimization in the situations where there are the goal regions for the respective responses. For such situations, we propose a multiresponse optimization method based on the generalized distance approach.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.165-194
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• 1999

Taguchi's parameter design seeks proper choice of levels of controllable factors (Parameters in Taguchi's terminology) that makes the qualify characteristic of a product optimal while making its variability small. This aim can be achieved by response surface techniques that allow flexibility in modeling and analysis. In this article, a collection of response surface modeling and analysis techniques is proposed to deal with the multiresponse optimization problem in experimentation with Taguchi's signal and noise factors.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.73-82
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• 2005

This article proposes a practical approach, which is based on the concept of the expected squared relative error, that can consider both the prediction quality and the practitioner's subjectivity in simultaneously optimizing multiple responses. Through a case study, multiresponse optimization using the expected squared relative error approach is illustrated, and the SAS program to implement the proposed method is provided.

• Journal of Dairy Science and Biotechnology
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• v.41 no.2
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• pp.45-56
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• 2023

This review serves two purposes: first, to promote the use of improved optimization techniques in response surface methodology (RSM); and second, to enhance the optimum conditions for the fermentation of probiotics. According to research in dairy science, Lactiplantibacillus plantarum K79 is a candidate probiotic that has beneficial health effects, such as lowering blood pressure. The optimum conditions for L. plantarumK79 to produce peptides with angiotensin-converting enzyme (ACE) inhibitory activity were proposed, through modeling each of ACE inhibitory activity and pH as a function of the four factors that are skim milk concentration (%), incubation temperature (℃), incubation time (hours), and starter added amount (%). To estimate optimum conditions, the researchers employed a desirability-based multi-response optimization approach, utilizing third-order models with a nonsignificant lack of fit. The estimated optimum fermentation conditions for L. plantarum K79 were as follows: a skim milk concentration of 10.76%, an incubation temperature of 36.9℃, an incubation time of 23.76 hours, and a starter added amount of 0.098%. Under these conditions, the predicted ACE inhibitory activity was 91.047%, and the predicted pH was 4.6. These predicted values achieved the objectives of the multi-response optimization in this study.