Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values

• Published in: Brazilian journal of microbiology Vol. 48; no. 2; pp. 352 - 358
• Main Authors: Gonçalves, Letícia Dias dos Anjos, Piccoli, Roberta Hilsdorf, Peres, Alexandre de Paula, Saúde, André Vital
• Format: Journal Article
• Language: English
• Published: Brazil Elsevier Editora Ltda 01-04-2017; Springer Nature B.V; Elsevier; Sociedade Brasileira de Microbiologia
• Subjects: Abuse; Aerobic microorganisms; Aerobiosis; Deterioration; Experimental data; Food; Food contamination & poisoning; Food Microbiology; Food Microbiology - methods; Hydrogen ions; Hydrogen-Ion Concentration; Mathematical models; Meat; Meat - microbiology; MICROBIOLOGY; Microorganisms; Modeling; Models, Theoretical; Nutrient availability; Off flavor; Perishable foods; pH effects; Proteolysis; Pseudomonas fluorescens; Pseudomonas fluorescens - drug effects; Pseudomonas fluorescens - growth & development; Pseudomonas fluorescens - radiation effects; Psychrotrophic microorganisms; Refrigeration; Slime; Spoilage; Temperature; Temperature effects; Water activity; Deterioration; Modeling; Meat
• ISSN: 1517-8382; 1678-4405; 1678-4405
• DOI: 10.1016/j.bjm.2016.12.006

Meat is one of the most perishable foods owing to its nutrient availability, high water activity, and pH around 5.6. These properties are highly conducive for microbial growth. Fresh meat, when exposed to oxygen, is subjected to the action of aerobic psychrotrophic, proteolytic, and lipolytic spoilage microorganisms, such as Pseudomonas spp. The spoilage results in the appearance of slime and off-flavor in food. In order to predict the growth of Pseudomonas fluorescens in fresh meat at different pH values, stored under refrigeration, and temperature abuse, microbial mathematical modeling was applied. The primary Baranyi and Roberts and the modified Gompertz models were fitted to the experimental data to obtain the growth parameters. The Ratkowsky extended model was used to determine the effect of pH and temperature on the growth parameter μmax. The program DMFit 3.0 was used for model adjustment and fitting. The experimental data showed good fit for both the models tested, and the primary and secondary models based on the Baranyi and Roberts models showed better validation. Thus, these models can be applied to predict the growth of P. fluorescens under the conditions tested.