Applied Food Biotechnology
  • Register
  • Login
  • English
    • فارسی
    • العربية
    • 简体中文
    • Español (España)
    • Français (France)
  • Home
  • Journal Info
    • About the journal
    • Editorial Team
    • Indexing & Abstracting
    • Privacy Statement
    • Reviewing Policies and Procedures
    • Plagiarism Policy
    • Archiving Policy
    • Journal History
  • Issues
    • Current
    • Archives
    • Accepted Manuscripts
    • In Press
  • Publication Ethics
  • Guideline For Authors
    • Submission
    • Journal Cover Letter
    • Copyright Form
    • Conflict of Interest
  • Contact Us
Advanced Search
  1. Home
  2. Archives
  3. Vol. 10 No. 1 (2023): Winter
  4. Original Article

Vol. 10 No. 1 (2023)

January 2023

Development of a Prediction Software for the Growth Kinetics of Pseudomonas spp. in Culture Media using Various Primary Models

  • Fatih Tarlak
  • Ozgun Yucel

Applied Food Biotechnology, Vol. 10 No. 1 (2023), 3 January 2023 , Page 1-8
https://doi.org/10.22037/afb.v10i1.39780 Published: 2023-01-03

  • View Article
  • Download
  • Cite
  • References
  • Statastics
  • Share

Abstract

 

Background and Objective: Pseudomonas spp. are bacteria with the widest effects on food spoilage. These bacteria can be found in several environments such as soil and water. The major purpose of this study was to develop a software; by which, the growth behaviours of Pseudomonas spp. in culture media could be predicted.

Material and Methods: A total number of 509 bacterial data points of Pseudomonas spp. in culture media were collected from the ComBase database. Temperature and pH were used as the major prediction variables for the description of Pseudomonas spp. behaviours in culture media. Modified Gompertz, Baranyi and Huang models, the most commonly used models in predictive food microbiology to predict the count of microorganisms, were used as well. Fitting capability of each model was assessed and compared with other capabilities considering their statistical indices of the root mean square error, RMSE; coefficient of determination, R2; corrected Akaike information criterion, AICc; and Bayesian information criterion, BIC.

Results and Conclusion: Huang model provided better predictions with 0.951 of R2 and 0.825 of RMSE, compared to those of traditionally used models. Prediction capability of the Huang model was assessed considering externally collected data from the ComBase database. Huang model in the validation process provided satisfactory statistical indices (bias factor = 1.027 and accuracy factor = 1.075). These results have revealed that Huang model can be reliably used as a model of describing the growth behaviours of Pseudomonas spp. Furthermore, developed software in this study includes significant potentials for predicting Pseudomonas counts in culture media.

Conflict of interest: The authors declare no conflict of interest.

Keywords:
  • ▪ Culture media ▪ growth kinetics ▪ predictive food microbiology ▪ Pseudomonas spp.
  • pdf

How to Cite

Tarlak, F., & Yucel, O. (2023). Development of a Prediction Software for the Growth Kinetics of Pseudomonas spp. in Culture Media using Various Primary Models . Applied Food Biotechnology, 10(1), 1–8. https://doi.org/10.22037/afb.v10i1.39780
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
  • Endnote/Zotero/Mendeley (RIS)
  • BibTeX

References

References

Tarlak F. Development of a novel growth model based on the central limit theorem for the determination of beef spoilage. Appl Food Biotechnol. 2021; 8(2): 143-150. https://doi.org/10.22037/afb.v8i2.33549

Hammond ST, Brown JH, Burger JR, Flanagan TP, Fristoe TS, Mercado-Silva N, Nekola JC, Okie J. Food spoilage, storage and transport: Implications for a sustainable future. Bio Sci. 2015; 65(8): 758-768.https://doi.org/10.5061/dryad.6708r

Braun PG. Modelling microbial food spoilage. In: Blackburn CW Edition Woodhead Publishing, Cambrigde. 2006: 86-118.

Bovill RA, Bew J, Baranyi J. Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature. II. Rapidly changing temperatures. Int J Food Microbiol. 2001; 67: 131-137.https://doi.org/10.1016/s0168-1605(01)00446-9

Rawat S. Food Spoilage: Microorganisms and their prevention. Asian J Plant Sci. 2015; 5(4):47-56.

Adams MR, Moss MO. Food Microbiology. Royal Society of Chemistry. Cambridge, 2000: 21-24.

Bezirtzoglou E, Maipa V, Voidarou C, Tsiotsias A, Papapetropoulou M. Food-borne intestinal bacterial pathogens. Anaerobe. 2009; 12(2): 96-104.https://doi.org/10.3402/mehd.v12i2.8073

Perez-Rodriguez F, Valero A. Predictive Microbiology in Foods. Springer, New York. 2013: 1-10.https://doi.org/10.1007/978-1-4614-5520-2

Fujikawa H. Prediction of Competitive Microbial Growth. Biocontrol Science. 2016; 21(4): 215-223. https://doi.org/10.4265/bio.21.215

Raposo A, Perez E, Tinoco de Faria C, Carrascosa C. Food Spoilage by Pseudomonas Spp-An Overview. In: Foodborne Pathogens and Antibiotic Resistance, Om V. Singh. John Wiley and Sons Editions, John Wiley and Sons Inc, Hoboken, New Jersey. 2016:41-71

Zwietering MH, Jongenburger I, ombouts FM, Van’t iet K. Modeling of the bacterial growth curve. Appl Environ Microb. 1990; 56:1875-1881.https://doi.org/10.1128/AEM.56.6.1875-1881.199012

Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol. 1994; 23: 277-294.https://doi.org/10.1016/0168-1605(94)90157013

Huang L. IPMP global Fit-A one-step direct data analysis tool for predictive microbiology. Int J Food Microbiol. 2017; 262: 38-48.

https://doi.org/10.1016/j.ijfoodmicro.2017.09.01014

Tarlak F: Development and validation of one-step modelling approach for the prediction of mushroom spoilage. J Food Nutr Res. 2020; 59: 281-289.

Acai P, Valik L, Medvedova A: One-and two-step kinetic data analysis applied for single and co-culture growth of Staphylococcus aureus, Escherichia coli and Lactic Acid Bacteria in milk. Appl Sci. 2021; 11(8): 8673. https://doi.org/10.3390/app11188673

Ratkowsky DA, Olley J, Mcmeekin TA, Ball A. Relationship between temperature and growth rate of bacterial cultures. J Bacteriol. 1982; 149: 1-5.https://doi.org/10.1128/JB.149.1.1-5.19827

Robinson TP, Ocio MJ, Kaloti A, Mackey BM. The effect of the growth environment on the lag phase of Listeria monocytogenes. Int J Food Microbiol. 1998; 44: 83-92.https://doi.org/10.1016/s0168-1605(98)00120-215

Oscar TP. Predictive model for growth of Salmonella Newport on Romaine lettuce. Journal of Food Safety, 2020; 40(3): e12786.1-9. https://doi.org/10.1111/jfs.12786

Manthou E, Tarlak F, Lianou A, Ozdemir M, Zervakis GI, Panagou EZ, Nychas GJE. Prediction of indigenous Pseudomonas spp. growth on oyster mushrooms (Pleurotus ostreatus) as a function of storage temperature. LWT. 2019; 111: 506-512.https://doi.org/10.1016/j.lwt.2019.05.062

  • Abstract Viewed: 860 times
  • pdf Downloaded: 593 times

Download Statastics

  • Linkedin
  • Twitter
  • Facebook
  • Google Plus
  • Telegram

Developed By

Open Journal Systems

Language

  • English
  • فارسی
  • العربية
  • 简体中文
  • Español (España)
  • Français (France)

Information

  • For Readers
  • For Authors
  • For Librarians
  • Home
  • Archives
  • Submissions
  • About the Journal
  • Editorial Team
  • Contact

AWT IMAGE

The journal of "Applied Food Biotechnology" is licensed under a  CC BY-NC 4.0. International License.

Powered by OJSPlus