Mathematical Modeling for the Growth of Salmonella spp. and Staphylococcus aureus in Cake at Fluctuating Temperatures
Source abstract
Abstract
This study aimed to develop dynamic mathematical models to predict the growth of Salmonella spp. and Staphylococcus aureus in a cake under fluctuating temperatures. Among the nine different types of cakes frequently served during school meals, one type of cake was selected based on bacterial growth and water activity. Cocktails of Salmonella spp. and S. aureus were inoculated in the samples and stored at 4–35 °C for up to 336 h. The growth of Salmonella spp. and S. aureus was observed above 20 and 15 °C, respectively. The bacterial cell counts were fitted in the Baranyi model, and the maximum specific growth rate (μmax; log CFU/g/h) and lag phase duration (LPD; h) were analyzed using a polynomial model as a function of temperature (R2 = 0.968–0.988), and the performance of the developed models was appropriate. Furthermore, dynamic models were developed, and the predictions were acceptable in changing the temperature, indicating that the developed dynamic models can successfully predict the outcomes of Salmonella spp. and S. aureus in cake. These results provide useful information for assessing and managing microbial risk in foods by predicting the behavior of Salmonella spp. and S. aureus in cake, especially in changing temperature.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Listeria monocytogenes in Food Safety · Microbial Inactivation Methods · Food Safety and Hygiene
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Korea Food Research Institute, 245 Nongsaengmyeong-ro, Wanju 55365, Korea
Department of Food Biotechnology, University of Science and Technology, 217 Gajeong-ro, Daejeon 34113, Korea
Funding
Korea Food Research Institute
References (25)
Show source-provided references
1. Canadian Food Inspection Agency (2021, March 02). Bacterial Pathogens in Pre-Prepared, Refrigerated Baked Desserts. 1 April 2016 to 31 March 2018. Food Microbiology-Targeted Surveys-Final Report. Available online: https://tinyurl.com/6h8cs3u.
2. Notes from the Field: Multistate Outbreak of Salmonella Agbeni Associated with Consumption of Raw Cake Mix—Five States, 201810.15585/mmwr.mm6834a5 ↗
3. A large outbreak of Salmonella enterica serovar Thompson infections associated with chocolate cake in Busan, Korea10.4178/epih.e2019002 ↗
4. An outbreak of Salmonella enterica serotype Enteritidis traced to cream cakes
5. Centers for Disease Control and Prevention (CDC) (2021, March 02). Outbreak of Salmonella Infections. Available online: https://tinyurl.com/y5ba37b8.
6. NSW Food Authority (2021, March 02). Microbiological Quality of High Risk Bakery Products. A Survey to Determine the Microbiological Quality of Bakery Products Sold in NSW. Available online: https://tinyurl.com/5d2u8tru.
7. Investi-gation of a Staphylococcal Food Poisoning Outbreak from a Chantilly Cream Dessert, in Umbria (Italy)10.1089/fpd.2016.2267 ↗
8. FSN (Food Safety News) (2021, March 02). Staph Found in Illinois Bakery Tied to Outbreak. Available online: https://tinyurl.com/rjw9a4mw.
9. Etiological Agents Implicated in Foodborne Illness World Wide10.5851/kosfa.2020.e75 ↗
10. EFSA (European Food Safety Authority) (2020, March 02). Salmonella the Most Common Cause of Foodborne Outbreaks in the European Union. Available online: https://tinyurl.com/34ujs4u3.
11. CDC (Center for Disease Control and Prevention) (2020, March 02). Salmonella and Food. Available online: https://tinyurl.com/36cct7yt.
12. CDC (Center for Disease Control and Prevention) (2020, March 02). Staphylococcal (Staph) Food Poisoning. Available online: https://tinyurl.com/5fraxfxw.
13. T Modeling of the Bacterial Growth Curve10.1128/aem.56.6.1875-1881.1990 ↗
14. Mathematical Models To Predict Kinetic Behavior and Growth Probabilities of Listeria monocytogenes on Pork Skin at Constant and Dynamic Temperatures10.4315/0362-028X.JFP-13-197 ↗
15. Dalgaard, P., and Mejlholm, O. (2018). Modeling Growth of Listeria and Lactic Acid Bacteria in Food Environments. Methods Mol. Biol., 247–264.10.1007/978-1-4939-9000-9_20 ↗
16. Modeling Microbial Growth Within Food Safety Risk Assessments10.1111/1539-6924.00299 ↗
17. Modelling the effect of osmotic adaptation and temperature on the non–thermal inactivation of Salmonella spp. on brioche-type products10.1016/j.ijfoodmicro.2019.02.010 ↗
18. Dynamic modeling of Listeria monocytogenes growth in pasteurized vanilla cream after postpro-cessing contamination10.4315/0362-028X-71.9.1828 ↗
19. Association of Official Analytical Chemists (AOAC) (2000). Official Methods of Analysis of AOAC International, AOAC International. [17th ed.].
20. A dynamic approach to predicting bacterial growth in food10.1016/0168-1605(94)90157-0 ↗
21. Effects of parameterization on the performance of empirical models used in ‘predictive microbiology’10.1006/fmic.1996.0011 ↗
22. Survival of Salmonella during Baking of Peanut Butter Cookies10.4315/0362-028X.JFP-13-408 ↗
23. Indices for performance evaluation of predictive models in food microbiology
24. Ministry of Food and Drug Safety (MFDS) (2021, January 18). Food code. Available online: http://www.foodsafetykorea.go.kr/foodcode/01_01.jsp.
25. Korea Meteorological Administration (2021, March 02). Climate Statistics Analysis. Available online: https://tinyurl.com/4pnukj5n.