Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Gunst, Richard F.

doi:10.1080/00401706.1996.10484509

Cited by 153 publications

(69 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can accurately represent the relationship between different factors and their responses. The impact of factors and their interactions on non-independent variables can be given, and the optimal conditions in multifactorial systems can be determined (Gunst, 1996). Therefore, RSM is widely used in the field of process optimization and control (Ghorbel-Bellaaj et al, 2011;Guerard et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Biological Preservative Formula forPenaeus vannameiby Response Surface Methodology

Wang

Tian

et al. 2017

Journal of Aquatic Food Product Technology

View full text Add to dashboard Cite

Penaeus vannamei is the most widely aquacultured shrimp in the world. However, it is vulnerable to bacteria spoilage. Recently, the use of biological preservatives to defend against bacteria has attracted much attention. However, the practical effects of these preservatives remain unclear. In this work, response surface methodology with Box-Behnken experimental design was adopted to investigate the effects of three biological preservatives (nisin, tea polyphenol (TP), and ε-polylysine (ε-PL)) on the bacterial growth in P. vannamei. The optimal preservative formula was obtained, which was nisin at 0.04 g/100mL, ε-PL at 0.48 g/100mL, and TP at 1.06 g/100mL. Under the optimal conditions, shrimp had a total bacteria number as low as 1.81 × 10 3 CFU/g, which was not significantly different to the practical determinations. The findings indicated that this model was effective to analyze the antimicrobial effects of preservatives and to optimize the combination of biological preservatives. Downloaded by [University of Sussex Library] at

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of the Biological Preservative Formula forPenaeus vannameiby Response Surface Methodology

Wang

Tian

et al. 2017

Journal of Aquatic Food Product Technology

View full text Add to dashboard Cite

show abstract

“…In addition, polynomial degrees can be high if the measurement points are much. Shortly, RSM is important in the field of food engineering since it provides enough information about the process and it has widespread use (Gunst, ).…”

Section: Introductionmentioning

confidence: 99%

Polynomial surface fitting and artificial neural networks‐based analysis of the storage days and garlic extract supplementation dependent microbial growths in minced raw chicken meat

Külcü

Kalkan

Akben

2019

J Food Process Preserv

View full text Add to dashboard Cite

In this study, effects of garlic extract on microbial growth occurred in minced chicken were measured. Microbial growths modeled were the yeast‐mold, Staphylococcus aureus, total coliform group bacteria, total mesophilic aerobic bacteria and total psychrophilic bacteria (TPB). Garlic extracts were added in concentrations of 0.5, 1, 1.5, and 2% and measurements were done at 1st, 3rd, 5th, 7th and 10th days of storage for each garlic extract. The polynomial surface fitting (PSF) and artificial neural networks (ANN) were used as modeling methods. It was found that the garlic extract has modelable effects to inhibit the microbial growth occurs in minced raw chicken meat. The fits of models were between 97 and 99%. Although the ANN provided better fit about 1–1.5%, the use of both models together was suggested due to some disadvantages of ANN. Finally, the garlic extract concentration that promises the best microbial growth inhibition was suggested using ANN and PSF models. Practical applications Garlic extract supplementation can completely prevent along first storage day all microbial growths in minced chicken meat stored at refrigerator temperature. The microbial growth can also be inhibited in later days, under same conditions. Thus, the shelf life of minced chicken meat stored at refrigerator temperature can be extended up to 1 day by means of garlic supplementation. In addition, the proposed models provide the possibility to calculate the microbial growth values as the function of storage day and garlic extract concentration.

show abstract

“…Process analytical technology and design of experiments (DOE) are the major tools under QbD paradigm that can facilitate its implemenation . DOE is an efficient and systematic approach of empirical modeling correlating process output responses, that is, product critical quality attributes (CQAs) to process input factors and is thus commonly used for optimization of multivariate processes . It aims to reduce the overall cost of screening, optimization, and characterization by guiding the number of experiments to be performed to a minimum and extracting the maximum information from them .…”

Section: Introductionmentioning

confidence: 99%

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments

Shekhawat

Godara

Kumar

et al. 2018

Biotechnology Progress

View full text Add to dashboard Cite

Development of a chromatographic step in a time and resource efficient manner remains a serious bottleneck in protein purification. Chromatographic performance typically depends on raw material attributes, feed material attributes, process factors, and their interactions. Design of experiments (DOE) based process development is often chosen for this purpose. A challenge is, however, in performing a DOE with such a large number of process factors. A split DOE approach based on process knowledge in order to reduce the number of experiments is proposed. The first DOE targets optimizing factors that are likely to significantly impact the process and their effect on process performance is unknown. The second DOE aims to fine‐tune another set of interacting process factors, impact of whom on process performance is known from process understanding. Furthermore, modeling of a large set of output response variables has been achieved by fitting the output responses to an empirical equation and then using the parametric constants of the equation as output response variables for regression modeling. Two case studies involving hydrophobic interaction chromatography for removal of aggregates and cation exchange chromatography for separation of charge variants and aggregates have been utilized to illustrate the proposed approach. Proposed methodology reduced total number of experiments by 25% and 72% compared to a single DOE based on central composite design and full factorial design, respectively. The proposed approach is likely to result in a significant reduction in resources required as well as time taken during process development. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2730, 2019

show abstract

Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Cited by 153 publications

References 0 publications

Optimization of the Biological Preservative Formula forPenaeus vannameiby Response Surface Methodology

Optimization of the Biological Preservative Formula forPenaeus vannameiby Response Surface Methodology

Polynomial surface fitting and artificial neural networks‐based analysis of the storage days and garlic extract supplementation dependent microbial growths in minced raw chicken meat

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments

Contact Info

Product

Resources

About