A dynamic approach to predicting bacterial growth in food

Baranyi, József; Roberts, T. A.

doi:10.1016/0168-1605(94)90157-0

Cited by 2,238 publications

(1,534 citation statements)

References 22 publications

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“…The growth and metabolic curves were modelled by means of the mechanistic model of Baranyi and Roberts (1994). The growth and metabolic parameters were calculated from each curve.…”

Section: Methodsmentioning

confidence: 99%

Development of new cereal-, pseudocereal-, and cereal-leguminous-based probiotic foods

Kocková¹,

Valı́k²

2014

Czech J. Food Sci.

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Kocková M., Valík Ľ. (2014): Development of new cereal-, pseudocereal-, and cereal-leguminous-based probiotic foods. Czech J. Food Sci., 32: 391-397.The suitability of the selected cereals, pseudocereals, and legumes for new probiotic foods development was tested. Probiotic products were produced by inoculating buckwheat, dark buckwheat, barley, oat, soya, and chickpea in combination with oat with lactobacillus rhamnosus GG and subsequent moulding to eliminate water from the cooked grains. The cell growth, pH and organic acid profiles were monitored during fermentation process at 37°C for 10 h followed by the storage period at 5°C for 21 days. The growth and metabolic parameters were calculated using principles of the predictive microbiology. lb. rhamnosus GG was able to grow in all substrates during fermentation and reached the cell density of 6.68-7.58 log CFU/g, the highest growth rate having been calculated in the oat product (0.341 log CFU/g/h). After the fermentation, the lowest pH value was observed in the barley product (4.52), while after the storage in the oat-soya product (4.32). The greatest amount of lactic acid after the storage period was measured in the oat-soya product (1977.8 mg/kg). Sensory characteristics of the fermented and stored products were also monitored.

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“…The growth and metabolic curves were modelled by means of the mechanistic model of Baranyi and Roberts (1994). The growth and metabolic parameters were calculated from each curve.…”

Section: Methodsmentioning

confidence: 99%

Development of new cereal-, pseudocereal-, and cereal-leguminous-based probiotic foods

Kocková¹,

Valı́k²

2014

Czech J. Food Sci.

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“…The culture growth was monitored after every 24-h interval at 600 nm by UV-Vis spectrophotometer (UV-2450 Shimadzu, Japan). The growth rate (l) and lag phase time (k) were calculated from plot of optical density (OD 600 nm) against time using the curvefitting DMFit programme (http://www.ifr.ac.uk/safety/ DMfit) (Baranyi and Roberts 1994).…”

Section: Growth Kinetics In Presence Of Zn and Zno Npsmentioning

confidence: 99%

Resistance of extremely halophilic archaea to zinc and zinc oxide nanoparticles

2015

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Industrialization as well as other anthropogenic activities have resulted in addition of high loads of metal and/or metal nanoparticles to the environment. In this study, the effect of one of the widely used heavy metal, zinc (Zn) and zinc oxide nanoparticles (ZnO NPs) on extremely halophilic archaea was evaluated. One representative member from four genera namely Halococcus, Haloferax, Halorubrum and Haloarcula of the family Halobacteriaceae was taken as the model organism. All the haloarchaeal genera investigated were resistant to both ZnCl 2 and ZnO NPs at varying concentrations. Halococcus strain BK6 and Haloferax strain BBK2 showed the highest resistance in complex/minimal medium of up to 2.0/1.0 mM ZnCl 2 and 2.0/1.0-0.5 mM ZnO NP. Accumulation of ZnCl 2 /ZnO NPs was seen as Haloferax strain BBK2 (287.2/549.6 mg g -1 ) [ Halococcus strain BK6 (165.9/388.5 mg g -1 ) [ Haloarcula strain BS2 (93.2/28.5 mg g -1 ) [ Halorubrum strain BS17 (29.9/16.2 mg g -1 ). Scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM-EDX) analysis revealed that bulk ZnCl 2 was sorbed at a higher concentration (21.77 %) on the cell surface of Haloferax strain BBK2 as compared to the ZnO NPs (14.89 %).

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“…The adjustment of the models to experimental data allows determination of relevant parameters such as the duration of the lag phase (lag) and the maximal specific growth rate (µ max ). The most common models were described and utilized, from the simplest exponential model to more complex ones such as Gompertz, logistic or Baranyi & Roberts models (Zwietering, Jongenburger, Rombouts, & Van't Riet, 1990;Baranyi & Roberts, 1994;Rosso et al, 1996). In the tutorials, students compared results obtained from the adjustment of different models when applied on a same data set.…”

Section: Tutorials To Learn Tools In Predictive Microbiologymentioning

confidence: 99%

Teaching microbiological food safety through case studies

2015

IJFS

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Higher education students usually ask for more training based on case studies. This was addressed by designing a specific food safety module (24 hours) in which students were shown how to predict microbiological risks in food products i.e. they were asked to determine product shelf-life according to product formulation, preservation methods and consumption habits using predictive microbiology tools. Working groups of four students first identified the main microbiological hazards associated with a specific product. To perform this task, they were given several documents including guides for good hygiene practices, reviews on microbiological hazards in the food sector, flow sheets, etc. . . After three-hours of work, the working groups prepared and gave an oral presentation in front of their classmates and professors. This raised comments and discussion that allowed students to adjust their conclusions before beginning the next step of their work. This second step consisted in the evaluation of the safety risk associated with the two major microbiological hazards of the product studied, using predictive microbiology. Students then attended a general lecture on the different tools of predictive microbiology and tutorials (6 hours) that made them familiar with the modelling of bacterial growth or inactivation. They applied these tools (9 hours) to predict the shelf-life of the studied product according to various scenarios of preservation (refrigeration, water activity, concentration of salt or acid, modified atmosphere, etc. . . ) and/or consumption procedures (cooking). The module was concluded by oral presentations of each working group and included student evaluation (3 hours).

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A dynamic approach to predicting bacterial growth in food

Cited by 2,238 publications

References 22 publications

Development of new cereal-, pseudocereal-, and cereal-leguminous-based probiotic foods

Development of new cereal-, pseudocereal-, and cereal-leguminous-based probiotic foods

Resistance of extremely halophilic archaea to zinc and zinc oxide nanoparticles

Teaching microbiological food safety through case studies

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