Modeling To Predict Growth/No Growth Boundaries and Kinetic Behavior of Salmonella on Cutting Board Surfaces

Abstract

This study developed models to predict the growth probabilities and kinetic behavior of Salmonella enterica strains on cutting boards. Polyethylene coupons (3 by 5 cm) were rubbed with pork belly, and pork purge was then sprayed on the coupon surface, followed by inoculation of a five-strain Salmonella mixture onto the surface of the coupons. These coupons were stored at 13 to 35°C for 12 h, and total bacterial and Salmonella cell counts were enumerated on tryptic soy agar and xylose lysine deoxycholate (XLD) agar, respectively, every 2 h, which produced 56 combinations. The combinations that had growth of ≥0.5 log CFU/cm(2) of Salmonella bacteria recovered on XLD agar were given the value 1 (growth), and the combinations that had growth of <0.5 log CFU/cm(2) were assigned the value 0 (no growth). These growth response data from XLD agar were analyzed by logistic regression for producing growth/no growth interfaces of Salmonella bacteria. In addition, a linear model was fitted to the Salmonella cell counts to calculate the growth rate (log CFU per square centimeter per hour) and initial cell count (log CFU per square centimeter), following secondary modeling with the square root model. All of the models developed were validated with observed data, which were not used for model development. Growth of total bacteria and Salmonella cells was observed at 28, 30, 33, and 35°C, but there was no growth detected below 20°C within the time frame investigated. Moreover, various indices indicated that the performance of the developed models was acceptable. The results suggest that the models developed in this study may be useful in predicting the growth/no growth interface and kinetic behavior of Salmonella bacteria on polyethylene cutting boards.

Optimization of drug combinations using Feedback System Control

“…Regression analysis can be performed in MATLAB 23 (Box 1; Supplementary Software using the file 'RegressionAnalysis.m'), R (Supplementary Software using the file 'RegressionDemo.R') or other appropriate programs. By using all data points obtained during the preceding optimization, a stepwise regression model 24 can be generated, which mathematically describes the cellular activity (in terms of the selected bioassay output) in response to the drug combinations administered to the cells (i.e., the system input).…”

Probabilistic Models to Predict Listeria monocytogenes Growth at Low Concentrations of NaNO2and NaCl in Frankfurters

“…In particular, use of probabilistic models is appropriate for determining ideal concentration combinations of NaNO 2 and NaCl for inhibiting the growth of foodborne pathogens in processed foods. A probabilistic model is developed with logistic regression analysis to produce interfaces between growth and no growth of bacteria under different environmental conditions ( Jo et al , 2014 ; Koseki et al , 2009 ; Yoon et al , 2012 ). Therefore, the objective of this study was to describe probabilistic models to predict L. monocytogenes growth responses in meat products formulated with low concentrations of NaNO 2 and NaCl.…”

Mathematical Model for Predicting the Growth Probability of Staphylococcus aureus in Combinations of NaCl and NaNO2under Aerobic or Evacuated Storage Conditions

“…Because the predictions from the developed probabilistic models were expressed as numbers, growth was determined at more than 0.5 of growth probability (Yoon et al , 2012). In addition, growth responses (growth or no growth) from the sausages were determined at 1 Log CFU/g of S. aureus growth (Gwak et al , 2015; Koutsoumanis et al , 2004).…”