Mathematical modelling and validation of Salmonella enterica growth in sushi exposed to different time-temperature scenarios found in real sushi establishments

Development and validation of a neural network model for growth of Salmonella Newport from chicken on cucumber for use in risk assessment

“…Thus, the model was successfully validated for interpolation because it satisfied all test data and model performance criteria of the APZ method in ValT. F I G U R E 1 Observed (symbols) and predicted (lines) growth of a chicken isolate of Salmonella Newport on cucumber as a function of time and temperature for representative data used in model development (16, 24, 32, 40℃) and validation (26, 38℃) Results of this study indicated that after simulated crosscontamination from utensils used to prepare raw chicken for cooking, Salmonella Newport (0.85 log) grew on cucumber portions (0.2 g) with native microflora at times (0 to 8 hr) and temperatures (16 to 40℃) observed during meal preparation and serving (da Silva et al, 2020). These results agree with other studies that investigated growth of Salmonella on cucumber slices held at room temperatures.…”

“…The predict function of NeuralTools was used to simulate growth of Salmonella Newport on cucumber as a function of time (0 to 8 hr) and temperature (16 to 40℃). In addition, to demonstrate how the model could be used in risk assessment, pert (minimum, most likely, maximum) distributions for times (0.5, 1, 6 hr) and temperatures (22, 25, 34℃) observed during meal preparation and serving (da Silva et al, 2020) were entered into the model and simulated with @Risk.…”

“…Thus, it is possible that isolates of Salmonella from chicken could cross-contaminate cucumbers in the field or during meal preparation and serving and then grow to high numbers and cause foodborne illness. Therefore, to provide a valuable tool for better assessing this risk to public health, this study was undertaken to develop and validate a predictive model for growth of a chicken isolate of Salmonella Newport on cucumber as a function of times (0 to 8 hr) and temperatures (16 to 40℃) observed during meal preparation and serving (da Silva et al, 2020) for use in risk assessment.…”

“…In addition, some neural network programs are compatible with Monte Carlo simulation software programs (e.g., @Risk) that allow use of probability distributions in the model for simulating variability and uncertainty of independent variables, which is an important characteristic of predictive models used in risk assessment (Oscar, 2009). Therefore, in this study, compatible software programs (i.e., Excel, NeuralTools, and @Risk) were used to develop a neural network model that could provide stochastic predictions of Salmonella growth on cucumber as a function of variable and uncertain times and temperatures observed during meal preparation and serving (da Silva et al, 2020).…”

Measurement Time Reduction by Means of Mathematical Modeling of Enzyme Mediated RedOx Reaction in Food Samples Biosensors

“…Exponential decrease equations model many chemical and biological processes [ 9 , 13 , 14 , 15 , 16 ], since in many cases the processes depend on the speed and the amount of sample that is present. These types of models are used whenever the velocity at which the reaction happens is proportional to the amount of existing sample.…”