Optimization of oyster-associated bacteria inactivation by dense phase carbon dioxide based on neural network

The inactivation of oyster-associated bacteria was investigated in order to explore the feasibility of oyster by dense phase carbon dioxide process. The process parameters were optimized by neural network and the neural network model was established. The results showed that when the temperature was (50±5)℃, significant bacteria inactivation effect was observed with low pressure and short time of DPCD treatment. When the temperature was lower than 45℃, temperature and pressure had significant effect on the bacteria inactivation of oyster. However, when the temperature was over 45℃, temperature, pressure and time had no significant effect on the bacteria inactivation of oyster. Exposing oysters to CO2 at 45℃ or 55℃, 15MPa for 30min induced 3-log reductions in the aerobic bacterial count, which was similar to that of oysters were treated at 100℃ for 2 min. The aerobic bacterial count of oysters treated by DPCD reached the standards of aquatic cooked products. The results provided theoretical basis for the bacteria inactivation of oyster by DPCD.