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Rheem S. Optimizing Food Processing through a New Approach to Response Surface Methodology. Food Sci Anim Resour 2023; 43:374-381. [PMID: 36909849 PMCID: PMC9998198 DOI: 10.5851/kosfa.2023.e7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 03/08/2023] Open
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 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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Affiliation(s)
- Sungsue Rheem
- Division of Big Data Science, Korea University, Sejong 30019, Korea
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Dang TD, Yong CC, Rheem S, Oh S. Optimizing the composition of the medium for the viable cells of Bifidobacterium animalis subsp. lactis JNU306 using response surface methodology. J Anim Sci Technol 2021; 63:603-613. [PMID: 34189508 PMCID: PMC8204007 DOI: 10.5187/jast.2021.e43] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 12/03/2022]
Abstract
This research improved the growth potential of Bifidobacterium
animalis subsp lactis strain JNU306, a commercial medium that is
appropriate for large-scale production, in yeast extract, soy peptone, glucose,
L-cysteine, and ferrous sulfate. Response surface methodology (RSM) was used to
optimize the components of this medium, using a central composite design and
subsequent analyses. A second-order polynomial regression model, which was
fitted to the data at first, significantly lacked fitness. Thus, through further
analyses, the model with linear and quadratic terms plus two-way, three-way, and
four-way interactions was selected as the final model. Through this model, the
optimized medium composition was found as 2.8791% yeast extract, 2.8030% peptone
soy, 0.6196% glucose, 0.2823% L-cysteine, and 0.0055% ferrous sulfate, w/v. This
optimized medium ensured that the maximum biomass was no lower than the biomass
from the commonly used blood-liver (BL) medium. The application of RSM improved
the biomass production of this strain in a more cost-effective way by creating
an optimum medium. This result shows that B. animalis subsp
lactis JNU306 may be used as a commercial starter culture
in manufacturing probiotics, including dairy products.
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Affiliation(s)
- Thi Duyen Dang
- Department of Animal Science, Chonnam National University, Gwangju 61186, Korea.,Western Highlands Agriculture and Forestry Science Institute, Buon Ma Thuot, Dak Lak Province 63161, Viet Nam
| | - Cheng Chung Yong
- Department of Animal Science, Chonnam National University, Gwangju 61186, Korea
| | - Sungsue Rheem
- Graduate School of Public Administration, Korea University, Sejong 30019, Korea
| | - Sejong Oh
- Department of Animal Science, Chonnam National University, Gwangju 61186, Korea
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Rheem S, Rheem I, Oh S. Improving the Quality of Response Surface Analysis of an Experiment for Coffee-supplemented Milk Beverage: II. Heterogeneous Third-order Models and Multi-response Optimization. Food Sci Anim Resour 2019; 39:222-228. [PMID: 31149664 PMCID: PMC6533392 DOI: 10.5851/kosfa.2019.e17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 11/29/2022] Open
Abstract
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.
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Affiliation(s)
- Sungsue Rheem
- Graduate School of Public Administration, Korea University, Sejong 30019, Korea
| | - Insoo Rheem
- Department of Laboratory Medicine, Dankook University Hospital, Cheonan 31116, Korea
| | - Sejong Oh
- Division of Animal Science, Chonnam National University, Gwangju 61186, Korea
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Rheem S, Oh S. 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. Food Sci Anim Resour 2019; 39:114-120. [PMID: 30882080 PMCID: PMC6411239 DOI: 10.5851/kosfa.2019.e9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 01/31/2019] [Accepted: 02/01/2019] [Indexed: 11/10/2022] Open
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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Affiliation(s)
- Sungsue Rheem
- Graduate School of Public Administration, Korea University, Sejong 30019, Korea
| | - Sejong Oh
- Division of Animal Science, Chonnam National University, Gwangju 61186, Korea
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Yoo H, Rheem I, Rheem S, Oh S. Optimizing Medium Components for the Maximum Growth of Lactobacillus plantarum JNU 2116 Using Response Surface Methodology. Korean J Food Sci Anim Resour 2018; 38:240-250. [PMID: 29805274 PMCID: PMC5960822 DOI: 10.5851/kosfa.2018.38.2.240] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 01/30/2018] [Accepted: 01/31/2018] [Indexed: 12/17/2022] Open
Abstract
This study was undertaken to find the optimum soy-peptone, glucose, yeast extract, and magnesium sulfate amounts for the maximum growth of Lactobacillus plantarum JNU 2116 and to assess the effects of these medium factors through the use of response surface methodology. A central composite design was used as the experimental design for the allocation of treatment combinations. In the analysis of the experiment, due to a significant lack of fit of the second-order polynomial regression model that was used at first, cubic terms were added to the model, and then two-way interaction terms were deleted from the model since they were found to be all statistically insignificant. A relative comparison among the four factors showed that the growth of L. plantarum JNU 2116 was affected strongly by yeast extract, moderately by glucose and peptone, and slightly by magnesium sulfate. The estimated optimum amounts of the medium factors for the growth of L. plantarum JNU 2116 are as follows: soy-peptone 0.213%, glucose 1.232%, yeast extract 1.97%, and magnesium sulfate 0.08%. These results may contribute to the production of L. plantarum L67 as a starter culture that may have potential application in yogurt and fermented meat products.
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Affiliation(s)
| | - Insoo Rheem
- Department of Laboratory Medicine, Dankook University Hospital, Cheonan 31116, Korea
| | - Sungsue Rheem
- Graduate School of Public Administration, Korea University, Sejong 30019, Korea
| | - Sejong Oh
- Division of Animal Science, Chonnam National University, Gwangju 61186, Korea
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Rheem S, Rheem I, Oh S. Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources. Korean J Food Sci Anim Resour 2017; 37:139-146. [PMID: 28316481 PMCID: PMC5355578 DOI: 10.5851/kosfa.2017.37.1.139] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 02/08/2017] [Indexed: 11/23/2022] Open
Abstract
Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.
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Affiliation(s)
- Sungsue Rheem
- Department of Applied Statistics, Korea University, Sejong 30019, Korea
| | - Insoo Rheem
- Department of Laboratory Medicine, Dankook University Hospital, Cheonan 31116, Korea
| | - Sejong Oh
- Division of Animal Science, Chonnam National University, Gwangju 61186, Korea
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Suh SH, Rheem S, Mah JH, Lee W, Byun MW, Hwang HJ. Optimization of production of monacolin K from gamma-irradiated Monascus mutant by use of response surface methodology. J Med Food 2007; 10:408-15. [PMID: 17887933 DOI: 10.1089/jmf.2006.097] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Monascus isolate number 711, which is capable of producing monacolin K as an inhibitor of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, the key enzyme of cholesterol synthesis, was isolated from Ang-kak, the red yeast rice koji. To increase the monacolin K-producing activity of the strain, spore suspensions of the strain were subjected to gamma-irradiation. One thousand mutants were generated via gamma-irradiation and screened using bioassay and high performance liquid chromatography analysis. Several mutants with higher productivities of monacolin K than that of the parent strain were primarily selected. Mutant KU609 was finally selected because of its characteristics of high monacolin K production and non-citrinin-producing activity under our test conditions. Response surface methodology was used to analyze the effect of culture medium on the production of monacolin K in mixed solid-state cultures. The optimal values of nutritional ingredients for the maximal production were soytone, glucose, MgSO4, and barley at concentrations of 0.5 g, 0.48 g, 0.053 g, and 9 g, respectively. The final monacolin K production of Monascus KU609 was increased almost 100-fold compared to that of the parent strain.
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Affiliation(s)
- Soo Hwan Suh
- Graduate School of Biotechnology, Korea University, Seoul, Republic of Korea
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Oh S, Worobo RW, Kim B, Rheem S, Kim S. Detection of the cholera toxin-binding activity of kappa-casein macropeptide and optimization of its production by the response surface methodology. Biosci Biotechnol Biochem 2000; 64:516-22. [PMID: 10803948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The cholera toxin (CT)-binding activity of purified kappa-casein macropeptide (CMP) from bovine kappa-casein was detected. In addition, a statistical model was developed to optimize the production of CMP. CMP was prepared by chymosin hydrolysis of kappa-casein and a subsequent 3% trichloroacetic acid treatment. CMP was further fractionated in an ion-exchange column by FPLC. CT binding activity was eluted at 0.18 M NaCl and was a single 8.9 kDa peptide without tyrosine and arginine residues. The CT binding activity was rapidly lost by a carbohydrase treatment. The conditions for CMP production with chymosin were optimized by using the response surface methodology (RSM). The estimated optimum levels of the factors were as follows: reaction temperature, 38.5 degrees C; pH, 6.44; and time, 35.9 min. A validation experiment was performed in which CMP was prepared under the predicted parameters, and it was ascertained that the estimated optimum conditions gave better production of CMP than any other conditions.
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Affiliation(s)
- S Oh
- Department of Food Science and Technology, New York State Agricultural Experiment Station, Cornell University, Geneva 14456, USA
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Oh S, Rheem S, Sim J, Kim S, Baek Y. Optimizing conditions for the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-glucose medium by using response surface methodology. Appl Environ Microbiol 1995; 61:3809-14. [PMID: 8526490 PMCID: PMC167683 DOI: 10.1128/aem.61.11.3809-3814.1995] [Citation(s) in RCA: 92] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
This study was undertaken to find optimum conditions of tryptone, yeast extract, glucose, Tween 80, and incubation temperature for the growth of Lactobacillus casei YIT 9018 and to assess the effects of these factors by use of response surface methodology. A central composite design was used as an experimental design for allocation of treatment combinations. A second-order polynomial regression model, which was used at first for analysis of the experiment, had a significant lack of fit. Therefore, cubic and quartic terms were incorporated into the regression model through variable selection procedures. Effects involving incubation temperature, yeast extract, glucose, and tryptone were significant, whereas the only significant effect involving Tween 80 was the interaction effect between temperature and Tween 80. It turned out that growth of L. casei YIT 9018 was most strongly affected by the incubation temperature. Estimated optimum conditions of the factors for growth of L. casei YIT 9018 are as follows: tryptone, 3.04%; yeast extract, 0.892%; glucose, 1.58%; Tween 80, 0%; incubation temperature, 35 degrees C.
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Affiliation(s)
- S Oh
- Hankuk Yakult Institute, Yong-in, South Korea
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