Hot water sanitization is a common means to maintain microbial control in process equipment for industries where microorganisms can degrade product or cause safety issues. This study compared the hot water inactivation kinetics of planktonic and biofilm-associated Sphingomonas parapaucimobilis at temperatures relevant to sanitization processes used in the pharmaceutical industry, viz. 65, 70, 75, and 80°C. Biofilms exhibited greater resistance to hot water than the planktonic cells. Both linear and nonlinear statistical models were developed to predict the log reduction as a function of temperature and time. Nonlinear Michaelis-Menten modeling provided the best fit for the inactivation data. Using the model, predictions were calculated to determine the times at which specific log reductions are achieved. While ≥80°C is the most commonly cited temperature for hot water sanitization, the predictive modeling suggests that temperatures ≥75°C are also effective at inactivating planktonic and biofilm bacteria in timeframes appropriate for the pharmaceutical industry.

No abstract

In microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880's, consists of diluting the samples several times and incubating each dilution to verify the existence of microbial Colony Forming Units or CFU's, seen by the naked eye. The main problem in the dilution series data analysis is the uncertainty quantification of the simple point estimate of the original number of CFU's in the sample (i.e., at dilution zero). Common approaches such as log-normal or Poisson models do not seem to handle well extreme cases with low or high counts, among other issues. We build a novel binomial model, based on the actual design of the experimental procedure including the dilution series. For repetitions we construct a hierarchical model for experimental results from a single lab and in turn a higher hierarchy for inter-lab analyses. Results seem promising, with a systematic treatment of all data cases, including zeros, censored data, repetitions, intra and inter-laboratory studies. Using a Bayesian approach, a robust and efficient MCMC method is used to analyze several real data sets.