The Predictive Food Microbiology Lab

Marisa Caipo

Sign-Aries
Favorite Journal-Theoretical Biology
Favorite Bacterium- Bacillus megaterium

Simulation of bacterial spore germination, outgrowth and lag, and cell doubling times at low inoculum levels and varying environmental conditions

Processed foods typically contain low numbers of bacterial spores. My research has shown previously that samples containing low spore numbers exhibit widely variable spoilage times. It is important to account for this variability if more realistic predictions are to be used to estimate time-to-spoilage in food products. My objectives were to model the variability caused by using small spore inocula with different combinations of pH and NaCl concentration and to develop a simulation to estimate spoilage time.

Fabiola Chea

Sign- Scorpio
Favorite Bacterium- Clostridium botulinum

Modeling the germination kinetics of Clostridium botulinum spores as affected by temperature, pH and sodium chloride

Predictive models developed for C. botulinum focus on the direct cause for concern (growth and toxin formation). Those models tend to be less accurate because they lump together a series of processes (i.e. germination, outgrowth, cell growth and toxin production) that react differently to changes in the environment. Although a germination model alone has little practical use, when used in combination with outgrowth and vegetative cell growth models, it can lead to systems of models with better predictive power.
My research involved modeling the germination kinetics of C. botulinum spores as a function of temperature, pH and NaCl. Germination in BHI broth (with chloramphenicol added to inhibit vegetative cell growth), was followed with phase-contrast microscopy. The germination kinetics at each environmental condition were best described by an exponential distribution. Quadratic polynomial models were developed by regression analysis to describe the mean germination time (r2=0.982) and germination extent (r2=0.867) as a function of temperature, pH and NaCl. Validation experiments confirmed that the model predictions were within an acceptable range compared to the experimental results and were fail-safe in most cases.

Lihui Zhao


Sign- Aries
Favorite Journals- Cell and National Geographic
Favorite Bacterium-Clostridium botulinum
Favorite Simpson's Character-Bart

Growth Kinetic of Clostridium botulinum

My project studies the growth kinetics of Clostridium botulinum. Four different factors are considered: pH, temperature, sodium chloride concentration and inoculum size. Each factor has three levels. Spores of C. botulinum 56A are inoculated in BHI broth, then 200 ml of the broth is distributed to each well on a 96 well microplate. Absorbance at 620nm is followed continuously. Absorbance data are fitted by Gompertz equation and growth rate is calculated.
SAS will be used to find the relationship between growth rates and the corresponding conditions. A simulation program will use the germination result from another student and growth rate result found here to give an estimation of time to spoilage under different pH, temperature, NaCl and inoculum size.

Siobain Duffy


Sign- Scorpio
Favorite Journal- Food Microbiology Online
Favorite Bacterium- E. coli MC1061 - great for plasmid transformation, protein overproduction
Favorite Simpsons Character-Dr. Nick


Quantitative Risk Assessment of E. coli O157:H7 in Apple Cider

One of the emerging trends in predictive food microbiology is the Quantitative Risk Assessment (QRA), which combines existing scientific literature, mathematics and computer simulation to produce multivariable models. Besides determining whether or not the processing of a food is safe, analysis of computer simulations reveals which particular steps in the processing are most critical to food safety - an invaluable aid in the creation of HACCP plans.
My QRA focuses on the risk of Escherichia coli O157:H7 in apple cider, which has caused outbreaks from coast to coast and has been given national attention. With the FDA's recently proposed rules on unpasteurized juices, which currently require a 5 log reduction of bacteria, the need to quantify the bactericidal and reductive steps in apple cider production becomes even more important. My model is being formulated in Microsoft Excel, the simulations are being run with an Excel add-in, @RISK (Palisade Corp.), and the model involves variables for contamination by animal manure, animals in the orchard and from unwashed equipment. Preliminary data suggest bacterial numbers can be reduced by the use of preservatives, temperature and sound washing and brushing practices.

Becky Montville

Sign-Leo
Favorite Journal-Morbidity and Mortality Weekly Report
Favorite Bacterium- Streptococcus pyogenes (it causes so many different diseases for just one organism)
Favorite Simpsons character- Otto Mann

Quantitative Risk Assessment of Handwashing in Foodservice

My quantitative risk assessment deals with the risk involved with different handwashing techniques in foodservice. I am comparing the effect of different types of soaps, hand sanitizers, and drying techniques on the microbial count on hands. The data I have collected thus far indicates that a structured hand washing system is more effective than an unstructured system. I have found that a combination of regular soap and hand sanitizer is most effective for removing bacteria from hands. Also, thorough drying with a paper towel reduces the microbial count on hands, while hot air dryers can increase it. I'm beginning to include other factors, such as glove and ring wearing, into my risk assessment.

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