General Regression Neural Network Model for Behavior of Salmonella on Chicken Meat during Cold Storage

Abstract

A model for predicting behavior of Salmonella on chicken meat during cold storage was developed and validated. The model will help the chicken industry to better predict and manage this risk to public health.

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

“…Performance of the model was evaluated using the test data and model performance criteria of the acceptable prediction zone (APZ) method (Oscar, ,b, ). The test data criteria are: (i) that the test data for validation for interpolation are collected using the same methods as those used to collect the data used to develop the model so as to provide a valid comparison of observed and predicted values; (ii) that the test data for validation for extrapolation are collected using the same methods as those used to collect the data used to develop the model with the exception of the independent variable being evaluated; and (iii) that the test data for interpolation or extrapolation provide uniform and complete coverage of model predictions so as to provide a complete and unbiased evaluation of model performance.…”

Neural network models for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment

“…The isolates were maintained in stock culture as previously described (Oscar, 2015). Precultures were incubated for 72 h at 22°C without shaking to obtain stationary-phase cells for inoculation of ground chicken thigh meat portions (0.75 cm 3 in 1.5 mL microcentrifuge tubes), which were prepared as previously described (Oscar, 2014a). Precultures were incubated for 72 h at 22°C without shaking to obtain stationary-phase cells for inoculation of ground chicken thigh meat portions (0.75 cm 3 in 1.5 mL microcentrifuge tubes), which were prepared as previously described (Oscar, 2014a).…”

“…After incubation of the RVB plate for 48 h at 42°C, Salmonella-positive (white) and Salmonellanegative (blue) wells were recorded and used to calculate the log MPN per portion by the method of Thomas (1942) as described by Oscar (2014a). After the first row was filled with samples, serial dilutions (1:10) were performed by a robotic pipettor (SOLO Plus, Hudson Robotics, Springfield, NJ, USA).…”

“…Stock cultures were thawed at room temperature, and then, 5 lL was added to 9 mL of buffered peptone water (BPW, Difco TM , Becton, Dickinson and Co., Sparks, MD, USA) in glass dilution tubes. Precultures were incubated for 72 h at 22°C without shaking to obtain stationary-phase cells for inoculation of ground chicken thigh meat portions (0.75 cm 3 in 1.5 mL microcentrifuge tubes), which were prepared as previously described (Oscar, 2014a). Precultures were serially diluted (1:10) in BPW, and then, 5 lL of the 10 À5 dilution of the appropriate serotype was inoculated into the centre of ground chicken portions at room temperature.…”

“…After incubation for 24 h at 40°C, 10 lL from each well of the BPW plate was transferred by the robotic pipettor to corresponding wells of a second deep-well plate that contained 1 mL of Rappaport Vassiliadis R10 broth (RVB; Difco TM , Becton, Dickinson and Co.) in each well. After incubation of the RVB plate for 48 h at 42°C, Salmonella-positive (white) and Salmonellanegative (blue) wells were recorded and used to calculate the log MPN per portion by the method of Thomas (1942) as described by Oscar (2014a). This automated mini-MPN method was validated in a previous study (Oscar, 2015) using an AOAC-approved lateral flow assay that showed complete concordance between white and blue wells in RVB and positive and negative results in the lateral flow assay, respectively.…”