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综合性深度学习框架预测基于脂管学对肉类营养生物标志物和代谢途径的防腐剂的研究
Wei Jia1,2,3,4, Aiai Guo1, Wenwen Bian2
1School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China.
Critical reviews in food science and nutrition
|December 21, 2023
概括
防腐剂改变了肉类的质量.
科学领域:
- 食品科学 食品科学 食品科学
- 生物化学 生物化学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 防腐剂可以延长肉类的保质期,但会影响味道和营养.
- 了解肉类分子转换的防腐效应至关重要.
研究的目的:
- 审查防腐剂对代谢途径和肉制品中的分子转变的影响.
- 探索OMIC技术和预测算法在肉质分析中的应用.
主要方法:
- 脂质组学,代谢组学和蛋白质组学分析被用来调查防腐剂的影响.
- 深度循环网络和转移学习 (DisoLipPred) 用于蛋白质序列分析.
- 包括神经网络在内的多式学习预测算法被考虑用于omics数据分析.
主要成果:
- 防腐剂通过改变pH值和离子强度来改变营养成分,影响酶活性.
- 盐诱导的离子强度会通过特定的脂酶触发甘油三的水解.
- 奥米克技术比传统方法更深入地了解防腐剂诱导的变化.
结论:
- 欧米克技术提供了一种强大的方法来阐明肉制品中的防腐机制.
- 使用神经网络的预测算法可以增强食品分析和发现.
- 这些进展可以推动新的策略,防止和控制肉类行业的食品腐败.
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