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.

The objectives of this study were to evaluate the antimicrobial effects of ε-poly-L-lysine mixture (EPM) on cheon-ggukjang and to determine the optimal mixing ratio in cheonggukjang. Increasing the EPM up to 2% decreased via-ble cell counts at both 15oC and 30oC storage. The pH of cheonggukjang without EPM increased slowly over thestorage periods, but cheonggukjang with EPM showed a slight increasing tendency, with the highest pH at 2% EPM.The sensory scores, such as texture and flavor, were highest in samples containing 2% EPM; however, overall pref-erence was not significantly different when compared to the control. For the optimum cheonggukjang processingconditions, 13 experimental points were selected. Soybean and EPM were chosen as independent variables. Viablecell count, pH, texture, and overall preference were measured. The optimum formulation of cheonggukjang usingthe numerical analysis was found to be 98.52% soybean and 1.48% EPM, resulting in a 0.722 desirability value.

The paper suggests making a policy and strategies for a way of exporting Korean ICT product effectively in the EU and Eastern area, and an effective preparation of 4th industrial revolution through analysis of preparation status of 4th industrial revolution of the Visegrad group. Analyze policy, status, what they want for 4th preparation in the Visegrad group from comparing characteristics analysis of each country’s official data, publication data, portal, paper, and etc. They have been preparing for 4th industrial revolution long time ago as basic research and business before 4th wave word. With these basic results, they are trying to apply such as, AI, S/W, security, ICT, etc. of 4th wave core technology. For the development of new export market in EU, the Korean team should research with university and research center or venture company. Through these cooperation, they should understand their personal characteristic, lifestyle, and what consumers want to purchase in EU. And this results can be used in South Asia and India that give a big effect to all over the world ICT market. The external impact of the 4th wave must have a long-term shift in manpower, and production policy is related to the EU's strategic role, or the preparation of the 4th wave to the V4 country in the short term.