农业工程学报2011，Vol.27Issue(12)：369-373,5.DOI:10.3969/j.issn.1002-6819.2011.12.069
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
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 (5O±5)°C, significant bacteria inactivation effect was observed with low pressure and short time of DPCD treatment. When the temperature was lower than 45 °C, temperature and pressure had significant effect on the bacteria inactivation of oyster. However, when the temperature was over 45 °C, temperature, pressure and time had no significant effect on the bacteria inactivation of oyster. Exposing oysters to CO2 at 45°C or 55°C, 15MPa for 30min induced 3-log reductions in the aerobic bacterial count, which was similar to that of oysters were treated at 100°C 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.关键词
神经网络/杀菌/优化/牡蛎/高密度CO2Key words
neural network/ sterilization/ optimization/ oyster/ dense phase carbon dioxide分类
轻工纺织引用本文复制引用
张良,刘书成,章超桦,吉宏武,高加龙,邓楚津..神经网络优化牡蛎的高密度CO2杀菌工艺[J].农业工程学报,2011,27(12):369-373,5.基金项目
现代农业产业技术体系专项基金(CARS-48-07B) （CARS-48-07B）
广东省自然科学基金(项目编号:10152408801000010) （项目编号:10152408801000010）
广东省水产蛋白改性技术研究团队(2011A020102005) （2011A020102005）
