Mathematical Models to Predict Staphylococcus aureus Growth on Processed Cheeses

Kim, Kyung Mi; Lee, Hee Young; Moon, Jin San; Kim, Young Jo; Heo, Eun Jeong; Park, Hyun Jung; Yoon, Yongik

doi:10.13103/jfhs.2013.28.3.217

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The minimum growth temperatures of S. aureus in grilled eggs, peeled quail eggs, and whole egg liquid were estimated to be 3.3, 4.5, and 5.2 °C, respectively. This result was similar to that of a growth prediction model for S. aureus in processed cheese that uses the square root (mozzarella: 6.3 °C, cheddar: 5.7 °C) (Kim et al, 2013). Also, the results of the growth prediction model for S. aureus in dried herring, which uses the square root, showed that the minimum growth temperature about 10 °C, indicating that bacterial growth was different depending on the food (Kang et al, 2013).…”

Section: Resultssupporting

confidence: 80%

“…Similar results were obtained when compared to other studies (Cho et al, 2011b) on the growth prediction model of L. monocytogenes in smoked salmon (A f : 1.03-1.58, B f : 1.01-1.55), a study (Kang et al, 2010) on the growth prediction of S. aureus and Bacillus cereus in RTE foods (A f : 1.04-1.37, Bf: 0.90-1.11), the growth prediction model (Park et al, 2009) of S. aureus in green-bean sprouts provided in school meals (A f : 1.10-1.31, Bf: 0.97-1.03, MSE: 0.002-0.02), and the suitability verification value (Yun et al, 2013) of the growth prediction model for pathogenic E. coli in paprika (A f : 1.04-1.18, B f : 0.98-1.00, MRE: -1.03, -0.04). RMSE for the growth prediction model for grilled eggs, peeled quail eggs, and the whole egg liquid was 0.16, 0.26, and 0.27, respectively, showing a value that is closer to 0 than 0.300-0.5344, the RSME value measured in the study (Kim et al, 2013) on the prediction of growth of S. aureus in cheese. It is similar to the RMSE value of 0.09-0.24 in the predictive model (Park et al, 2010) of S. aureus growth in boiled meat, showing a high suitability.…”

Section: Resultsmentioning

confidence: 61%

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Predictive model of Staphylococcus aureus growth on egg products

Choi

Son

Cho

et al. 2018

Food Sci Biotechnol

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Egg products are widely consumed in Korea and continue to be associated with risks of Staphylococcus aureus-induced food poisoning. This prompted the development of predictive mathematical models to understand growth kinetics of S. aureus in egg products in order to improve the production of domestic food items. Egg products were inoculated with S. aureus and observe S. aureus growth. The growth kinetics of S. aureus was used to calculate lag-phase duration (LPD) and maximum specific growth rate (l max ) using Baranyi model as the primary growth model. The secondary models provided predicted values for the temperature changes and were created using the polynomial equation for LPD and a square root model for l max . In addition, root mean square errors (RMSE) were analyzed to evaluate the suitability of the mathematical models. The developed models demonstrated 0.16-0.27 RMSE, suggesting that models properly represented the actual growth of S. aureus in egg products.

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Section: Resultssupporting

confidence: 80%

Section: Resultsmentioning

confidence: 61%

Predictive model of Staphylococcus aureus growth on egg products

Choi

Son

Cho

et al. 2018

Food Sci Biotechnol

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“…According to Table 1, the lag phase of S. aureus at 26 °C showed the highest value (4.80 ± 0.07), inversely proportional to ISSN (print) temperature, with the highest μmax at 35 °C (1.05 ± 0.03). These findings are similar to those in previous reports, which showed that the maximum growth rate increased proportionally to the increase in the temperature (Kim et al, 2013;Mansur et al, 2016;Choi et al, 2018). The term of "h0" is a parameter of the Baranyi model that attempts to consider the physiological status of bacteria during growth (Baranyi and Roberts, 1994).…”

Section: Primary Modelsupporting

confidence: 90%

Predictive modelling of Staphylococcus aureus growth on beef rendang

Anandita, A. F.,

Arief, I. I.,

Taufik, E.

2024

MJM

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Aims: This study was aimed to develop predictive mathematical models to understand the growth kinetics and growth curves of Staphylococcus aureus in beef rendang at 26 °C and 35 °C temperatures. Methodology and results: Staphylococcus aureus ATCC 25923 was inoculated with 0.1 mL of each broth culture to beef rendang in a sterile jar. Each sample was dried in a laminar flow hood for 1 h to allow attachment of the bacteria. After 1 h, the samples were stored at 26 °C and 35 °C. During storage, each sample was analyzed at 3 h intervals for the samples stored at 35 °C and 6 h intervals at 26 °C. The results of cell concentration during storage were fitted to Baranyi, Huang and modified Gompertz models as the primary growth model and developed using the IPMP 2013 software. The Ratkowsky model was used as a secondary model to fit the maximum specific growth rate. The modified Gompertz showed the best result in fitting the μmax growth in the primary model. The model has the best fitting for the primary model and secondary model. The model suggests that S. aureus grows faster at 35 °C in beef rendang. Conclusion, significance and impact of study:The findings of this study showed the lag phase and μmax values, which could be used for determining the best storage temperature between 26 °C and 35 °C for beef rendang, which is safe to be consumed. Rendang should not be consumed after 6 h at 35 °C and 16 h in 26 °C.

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“…For Mozzarella ( A w =0·970) and Cheddar cheeses ( A w =0·965), T min values were 6·35, and 5·72, respectively (Kim et al 2013). Because the T min values from the study by Kim et al (2013) were estimated only at high A w , the T min values were lower than that of our study. These results were impacted by the kinetic behaviour of pathogens, which depends on the food matrix (Yoon, 2010).…”

Section: Resultsmentioning

confidence: 99%

Kinetic behaviour ofStaphylococcus aureuson cheese as a function of water activity and temperature

Lee

Kim

Lee

et al. 2014

Journal of Dairy Research

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This study developed mathematical models in order to evaluate the effect of Aw (Water activity) and growth temperature on Staphylococcus aureus kinetic behaviour. The Aw levels (0·970, 0·975, 0·983, and 0·991) of cheese were adjusted by NaCl; then, Staph. aureus was inoculated on the cheese, followed by storage at 7-30 °C for 72-720 h. Total bacterial and Staph. aureus cell counts were enumerated on tryptic soy agar and mannitol salt agar, respectively. The Baranyi model was fitted to the Staph. aureus growth data in order to calculate the maximum specific growth rate (μmax; log CFU/g/h), lag phase duration (λ; h), lower asymptote (N0; log CFU/g) and upper asymptote (Nmax; log CFU/g). The effects of storage temperature and Aw on the kinetic parameters (μmax and λ) were then further analysed with the Ratkowsky-type model and a polynomial equation, respectively. The root mean square error (RMSE) and relative error (RE) were calculated in order to estimate the model performance. No significant effect of Aw on Staph. aureus growth was observed at 7 °C; thus, the Baranyi model was fitted to the growth data from 15, 25 and 30 °C. The μmax values (0·011-0·303 log CFU/g/h) increased (P<0·05) as the storage temperature and Aw increased. In addition, λ values (2·42-63·48 h) decreased (P<0·05) as storage temperature and Aw increased; yet, the effect of Aw on λ was observed only at 15 °C. The theoretical minimum storage temperature and Aw were 10·15 °C and 0·882, respectively. RMSE (0·010-1·544) and RE values (-0·131 to 0·187) from validation indicated that model performance was appropriate. Hence, these results suggest that the developed models in this study should be useful in describing the effect of temperature and Aw on the growth kinetic behaviour of Staph. aureus in cheese along with the exposure assessment of Staph. aureus in cheese as well.

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Mathematical Models to Predict Staphylococcus aureus Growth on Processed Cheeses

Cited by 5 publications

References 17 publications

Predictive model of Staphylococcus aureus growth on egg products

Predictive model of Staphylococcus aureus growth on egg products

Predictive modelling of Staphylococcus aureus growth on beef rendang

Kinetic behaviour ofStaphylococcus aureuson cheese as a function of water activity and temperature

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