神经网络优化牡蛎的高密度CO2杀菌工艺

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

    • 摘要: 为了研究高密度二氧化碳(DPCD)对牡蛎的杀菌效果,利用神经网络对DPCD杀菌工艺参数进行优化,建立了杀菌工艺的神经网络模型。研究结果表明:当温度在(50±5)℃时,压力和时间相应调整,即可在低压短时间内达到较好的杀菌效果;在低于45℃条件下,温度和压力对DPCD杀菌效果影响较大;在高于45℃条件下,温度、压力和处理时间对DPCD杀菌效果的影响较小;在45或55℃和15 MPa条件下,DPCD直接对牡蛎肉处理30 min,其杀菌效果与100℃沸水煮2 min相当,菌落总数下降了3.0个对数以上,达到了水产熟制品卫生标准。该研究为牡蛎DPCD杀菌提供了理论依据。

       

      Abstract: 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.

       

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