The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Response surface methodology: A review on its applications and challenges in microbial cultures
Abstract
Abstract Response surface methodology (RSM) was effectively used for the optimization and modelling of a wide variety of microbial products. This method is a combination of statistical and mathematical technique for model construction, assessing the effect of several independent variables and getting optimum values of variables. The purpose of this paper is to review recent research on the utilization of response surface methodology in the fermentation process to optimize microbial activities and products. In addition to discussing the usefulness of using this method, this article also reviews the current information and the hypothetical concepts as well as the main steps for the application of RSM in microbial cultures. Furthermore, this paper attempts to show the recent implementing studies as well as the challenges that may occur on using response surface methodology as an effective technique to develop the performance of industrial microbial strains.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008) · Talanta · doi:10.1016/j.talanta.2008.05.019 · Cited by 6,075
On the Experimental Attainment of Optimum Conditions
Box, G. E. P., & Wilson, K. B. (1951) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1951.tb00067.x · Cited by 4,464
Some New Three Level Designs for the Study of Quantitative Variables
Box, G. E. P., & Behnken, D. W. (1960) · Technometrics · doi:10.1080/00401706.1960.10489912 · Cited by 4,177
Modeling and optimization I: Usability of response surface methodology
Baş, D., & Boyacı, İ. H. (2006) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2005.11.024 · Cited by 2,209
Ghafari, S., Aziz, H. A., Isa, M. H., & Zinatizadeh, A. A. (2009) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2008.07.090 · Cited by 623
Li, C., Bai, J., Cai, Z., & Ouyang, F. (2002) · Journal of Biotechnology · doi:10.1016/s0168-1656(01)00377-7 · Cited by 243
Sarabia, L. A., & Ortiz, M. C. (2009) · Elsevier eBooks · doi:10.1016/b978-044452701-1.00083-1 · Cited by 236
Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN
Geyikçi, F., Kılıç, E., Çoruh, S., & Elevli, S. (2011) · Chemical Engineering Journal · doi:10.1016/j.cej.2011.12.019 · Cited by 198
Dual Response Surface Optimization: A Fuzzy Modeling Approach
Kim, K.-J., & Lin, D. K. J. (1998) · Journal of Quality Technology · doi:10.1080/00224065.1998.11979814 · Cited by 194
Kunamneni, A., Ellaiah, P., Srinivasulu, B., Devi, R. B., & Adinarayana, G. (2003) · Process Biochemistry · doi:10.1016/s0032-9592(03)00057-8 · Cited by 179
Research works printed but not linked: 11
- Mahrous, 2013, Food Nutri Sci, 4, 342
- Ramirez, 2000, CyTA-J. Food, 3, 21
- Marichamy, 2020, Mater. Today: Proc., 24, 1400
- G.E.P. Box, J.S. Hunter, W.G. Hunter, Wiley-Interscience: Hoboken, NJ, USA, 2005, pp. 47, 455.
- B.S.M. AL-Saeedi, K.J.K. Luti, Iraqi J. of Science, 59(2018), 463-475.
- Dyaee, 2019, Iraqi J. of, Science, 60, 494
- Yonis, 2019, Iraqi J. of, Agricultural Sciences, 50, 916
- Mohammed, 2020, AIP Conf. Proc., 2213
- D. Pritima, J. Vairamuthu, P. Gopi Krishnan, S. Marichamy, B. Stalin, S. Sheeba Rani ,Materials Today: Proceedings,2020,ISSN 2214-7853
- Khoo, 2001, Int. J. Adv. Manuf. Tech., 18, 483, 10.1007/s0017010180483 · DOI
- Lima, 2010, J. Contiero Food Technology and Biotechnology, 48, 175
Cited by
Naveed, M., Tianying, H., Wang, F., Yin, X., Chan, M. W. H., Ullah, A., Xu, B., Aslam, S., Ali, N., Abbas, Q., et al. (2022) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2022.06.002 · Cited by 26
Gracious, M., & Nandakumar, S. (2024) · Journal of Applied Biology & Biotechnology · doi:10.7324/jabb.2024.177031 · Cited by 2
“In essence, response surface methodology is a combination of steps comprising experimental design, mathematical modeling, and statistical inference that, when used together, enable the researcher to analyze the response that is influenced by a number of variables [16].”
Metadata
- Authors
- Breig, S. J. M., & Luti, K. J. K.
- Affiliations
- University of Baghdad (IQ)
- Corresponding author
- Breig, Sura Jasem Mohammed
- Venue
- Materials Today: Proceedings
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 42
- Pages
- 2277-2284
- ISSN
- 2214-7853 (ISSN-L)
- OpenAlex
- W3125806872
- Cited by
- 281
- References
- 25
- Citations by year
- 2026: 46 · 2025: 78 · 2024: 61 · 2023: 49 · 2022: 34 · 2021: 13
- FWCI
- 23.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- Materials Today Proceedings (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Life Sciences › Agricultural and Biological Sciences › Food Science › Advanced Scientific Research Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- response surface methodology, microbial fermentation, process optimization, fermentation process modeling, multivariate optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
