Optimization of a new mathematical model for bacterial growth

Huang, Lihan

doi:10.1016/j.foodcont.2012.11.019

Cited by 106 publications

(72 citation statements)

References 6 publications

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“…It is characterized by the following equations:

y_{t} = y_{0} + y_{\max} - \ln \{e^{y_{0}} + [e^{y_{\max}} - e^{y_{0}}] e^{μ_{\max} B false(t false)}\}

where

B false(t false) = t + \frac{1}{α} \ln \frac{1 + e^{- α (t - λ)}}{1 + e^{α λ}}

y_{t}

represents the cell concentration in ln CFU/g at time t ;

y_{0}

represents the initial cell concentration in ln CFU/g; y max represents the maximum cell concentration in ln CFU/g; μ max is the maximum specific growth rate in ln CFU/h; λ is the duration of the lag phase in hours. An α value of four was used as recommended by Huang ().…”

Section: Methodsmentioning

confidence: 99%

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

Juneja¹,

Golden

Mishra

et al. 2019

Journal of Food Science

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A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat‐shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R2 values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single‐rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (−1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. Practical Application Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.

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“…It is characterized by the following equations:

y_{t} = y_{0} + y_{\max} - \ln \{e^{y_{0}} + [e^{y_{\max}} - e^{y_{0}}] e^{μ_{\max} B false(t false)}\}

where

B false(t false) = t + \frac{1}{α} \ln \frac{1 + e^{- α (t - λ)}}{1 + e^{α λ}}

y_{t}

represents the cell concentration in ln CFU/g at time t ;

y_{0}

Section: Methodsmentioning

confidence: 99%

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

Juneja¹,

Golden

Mishra

et al. 2019

Journal of Food Science

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“…The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/ 2007-2013, coordinated by Irstea (Anthony, France). The authors are thankful to the Sym'Previus consortium (www.symprevius.org) for making available the L. monocytogenes strain used for this study.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Predictive modelling is one of these tools. Several predictive models have been developed in order to quantify the effect of various factors on L. monocytogenes load evolution in various food products (Rosso et al, 1996;Dalgaard and Vigel Jørgensen, 1998;Zuliani et al, 2007;Couvert et al, 2010;Mejlholm et al, 2010;Huang, 2013;Polese et al, 2014). These mathematical models were based on the different phases of bacterial growth: lag; exponential; and stationary (Buchanan, 1993).…”

Section: Introductionmentioning

confidence: 99%

Safety and quality assessment of ready-to-eat pork products in the cold chain

Stahl¹,

Ndoye²,

Jabri

et al. 2015

Journal of Food Engineering

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“…1 H is the first harmonic signal of the same position of second harmonic signal , K is the scale factor and X is the concentration of .…”

Section: Wavelength-modulation Spectroscopy (Wms)mentioning

confidence: 99%

“…Prediction of microorganism growth is a promising field of food microbiology that has been graining an increased significance as a tool for risk analysis and shelf life prediction [1]. In predictive microbiology, a mathematical model is frequently used to describe the behavior of microorganisms under different physical or chemical conditions such as temperature, pH, and water activity [2].…”

Section: Introductionmentioning

confidence: 99%

A novel measurement method of microorganism growth by tunable diode laser-absorption spectroscopy

et al. 2015

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The objective of this work was to attain essential parameters by using a Gompertz model that employed a new approach of wavelength modulation spectroscopy (WMS) to describe the microorganism growth. The measurement method of WMS introduces noninvasive technique instead of complicated invasive microorganism operation analysis and quickly obtains the accurate real-time measurement results. By using the WMS measurement, the specific growth curve of microorganism growth clearly displayed every three minute, which has characteristics of high sensitivity, high spectral resolution, fast time response and overcomes the randomness and error operation of traditional analysis methods. The measurement value of BF and AF in the range of 1.008 to 1.043 and the lower MSE showed that Gompertz model can fit the data well and be capable of describing bacteria growth rate and lag time. The results of experiment data suggested that the specific growth rate of microorganism depends on the temperature. With the increase of temperature ranging from 25 o C to 42 o C , the lag time of bacteria growth has been shortened. And the suitable temperature of bacteria growth is about 37 o C . Judging from the growth rate of microorganisms, we can identify the microbial species, not only to improve the precision and efficiency, but also to provides a rapidly sensitive way for microbial detection. The lag time of microorganism growth also provides a great application prospect for shelf life of the food safety.

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Optimization of a new mathematical model for bacterial growth

Cited by 106 publications

References 6 publications

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

Safety and quality assessment of ready-to-eat pork products in the cold chain

A novel measurement method of microorganism growth by tunable diode laser-absorption spectroscopy

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