The secondary growth model for Salmonella was developed based on the artificial neural network (ANN) with data collected from ComBase and FoodData Central. In addition to the existing secondary model variables (temperature, pH, Na+, and water contents), more input variables (sugar, carbohydrate, lipid, and protein contents) were considered. The output variables were microbial growth parameters (lag phase duration [l] and maximum growth rate [mmax]). A commercial ANN program (NeuralWorks Predict) was utilized with training at 80%, validation at 10%, and test data at 10%. ANN models were created using all data and cleansed data. Using the cleansed data, the training/testing root mean square error (RMSE) for mmax improved from 0.14/0.16 to 0.11/0.14, whereas the RMSE for l was still not acceptable, from 11.94/33.03 to 7.09/4.18. The l data were divided into two ranges with high and low goodness of fit, whereas the ANN model for each field was built, resulting in an optimally low RMSE.

Introduction
Materials and Methods
    Data collection
    Data cleansing
    ANN modeling
Results and Discussion
    Data for ANN
    Data cleansing
    ANN model
    Accuracy of ANN model
Acknowledgment
References
Author Information

• Yong Keun Koo(Department of Food Science and Biotechnology, Dongguk University)
• Yong Woon Jung(Department of Food Science and Biotechnology, Dongguk University)
• Dong Hwa Kim(Department of Food Science and Biotechnology, Dongguk University)
• Sang Won Kim(Department of Food Science and Biotechnology, Dongguk University)
• Eun Seol Kim(Department of Food Science and Biotechnology, Dongguk University)
• Byeong Jae Park(Department of Food Science and Biotechnology, Dongguk University)
• Seung Ju Lee(Department of Food Science and Biotechnology, Dongguk University)
• Seung Won Jung(Department of Food Science and Biotechnology, Dongguk University) Corresponding author