Predictive model of Staphylococcus aureus growth on egg products

Choi, Won-Seok; Son, Na-Ri; Cho, Jun-Il; Joo, In-Sun; Han, June Hyun; Kwak, Hyo‐Sun; Hong, Jin-Hwan; Suh, Soo Hwan

doi:10.1007/s10068-018-0529-4

Cited by 18 publications

(14 citation statements)

References 30 publications

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“…However, the values were still close to zero. The model is considered to be more suitable, as the RMSE value is closer to zero [54]. A similar pattern of higher RMSE index values in the case of G. candidum growth in milk with 1% NaCl was also demonstrated in the secondary modelling.…”

Section: Statistical Evaluation and Validation Of Modelssupporting

confidence: 66%

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Quantitative Characterization of Geotrichum candidum Growth in Milk

Šipošová¹,

Koňuchová²,

Valı́k³

et al. 2021

Applied Sciences

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The study of microbial growth in relation to food environments provides essential knowledge for food quality control. With respect to its significance in the dairy industry, the growth of Geotrichum candidum isolate J in milk without and with 1% NaCl was investigated under isothermal conditions ranging from 6 to 37 °C. The mechanistic model by Baranyi and Roberts was used to fit the fungal counts over time and to estimate the growth parameters of the isolate. The effect of temperature on the growth of G. candidum in milk was modelled with the cardinal models, and the cardinal temperatures were calculated as Tmin = −3.8–0.0 °C, Topt = 28.0–34.6 °C, and Tmax = 35.2–37.2 °C. The growth of G. candidum J was slightly faster in milk with 1% NaCl and in temperature regions under 21 °C. However, in a temperature range that was close to the optimum, its growth was slightly inhibited by the lowered water activity level. The present study provides useful cultivation data for understanding the behaviour of G. candidum in milk and can serve as an effective tool for assessing the risk of fungal spoilage, predicting the shelf life of dairy products, or assessing the optimal conditions for its growth in relation to the operational parameters in dairy practices.

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Section: Statistical Evaluation and Validation Of Modelssupporting

confidence: 66%

“…However, the values were still close to zero. The model is considered to be more suitable, as the RMSE value is closer to zero [54].…”

Section: Statistical Evaluation and Validation Of Modelsmentioning

confidence: 99%

Quantitative Characterization of Geotrichum candidum Growth in Milk

Šipošová¹,

Koňuchová²,

Valı́k³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…This methodology was occasionally already applied in the 1980s and 1990s, but has been widely used in recent literature. An extensive list of example studies exploiting this approach is provided in Table 1 , both for microbial growth [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] and thermal inactivation [ 55 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], with a focus on early and recent examples. Interestingly, however, this predictive microbiology approach bears some similarities to the traditional challenge testing approach, in which microbial growth/inactivation experiments were also conducted directly in/on the food product of interest [ 5 ].…”

Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Verheyen

Impe

2021

Foods

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Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10–20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.

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“…Bf represents the relative deviation among observed and predicted; moreover, this parameter allows for determining whether the model over or under-predicts microbial growth (Dalgaard and Jorgensen, 1998). For instance, a Bf value outside the range 0.7 to 1.5 indicates that the model is unsuitable (Choi et al, 2019;Ross, 1996;1999). A perfect agreement between predictions and observations must have values of Af and Bf equal to 1.0 (Choi et al, 2019;Ross, 1999).…”

Section: Secondary Modelingmentioning

confidence: 99%

A predictive growth model for Yarrowia lipolytica ATCC 9773 in wastewater

2021

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This study focuses on the development of a secondary model for Yarrowia lipolytica in a sewage treatment process. The raw data of Y. lipolytica growth were adjusted to the Buchanan model in order to obtain growth parameters such as initial count cells (Y0), maximum specific growth rate (μmax), latency phase (λ) and maximum cell population (Ymax). The µ values obtained at different pH levels (5.0 to 8.0) were used to build the secondary model based on a linear equation. The results showed a significant effect of pH on µmax values. The validation process of the developed models displays accuracy (Af) and bias factor (Bf) values close to one, while the values of root mean square error (RMSE) were low, confirming that such models can predict the growth of Y. lipolytica in dairy wastewater. This can be interesting to optimize sewage treatments that involve this kind of microorganism. Moreover, the dairy wastewater was a good substrate to support the Yarrowia lipolytica's growth and could be used to produce enzymes.

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Predictive model of Staphylococcus aureus growth on egg products

Cited by 18 publications

References 30 publications

Quantitative Characterization of Geotrichum candidum Growth in Milk

Quantitative Characterization of Geotrichum candidum Growth in Milk

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

A predictive growth model for Yarrowia lipolytica ATCC 9773 in wastewater

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