基于Ramanomes技术的乳酸细菌和酵母的快速选和识别策略及其在发酵食品中的应用
Shijie Liu1, Lijun Zhao1, Miaoyun Li1
1College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, PR China; International Joint Laboratory of Meat Processing and Safety in Henan Province, Henan Agricultural University, Zhengzhou 450002, PR China.
Food research international (Ottawa, Ont.)
|November 27, 2024
概括
这项研究引入了一种使用Ramanomes技术的快速无培养方法,用于识别发酵食品中的益生菌和酵母. 这项创新显著提高了益生菌资源开发的初步查效率.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 生物技术是生物技术.
背景情况:
- 传统的益生菌查依赖于耗时的培养方法.
- 需要快速,灵敏和具有成本效益的方法来识别益生菌.
- 益生菌资源开发需要有效的初步选技术.
研究的目的:
- 开发一种无培养,快速的"分离-丰富-检测"方法,用于在发酵食品中识别益生菌.
- 使用AgNPs NSA芯片建立乳酸细菌和酵母的Ramanome参考.
- 克服传统的"在选之前培养"范式的局限性.
主要方法:
- 使用Ramanomes技术与银纳米粒子纳米结构阵列 (AgNPs NSA) 芯片作为SERS基材.
- 采用系统的集群分析来区分细菌和酵母属及其亲属关系.
- 应用卷积神经网络用于识别,并通过梯度离心技术开发了隔离/丰富.
主要成果:
- 使用卷积神经网络实现了100%的识别精度和低于10 CFU/mL的灵敏度.
- 在不同的乳酸细菌和酵母菌的不同属和物种之间明确区分.
- 获得的乳酸菌和酵母的平均回收率大于98%,经过100%的验证分类准确度.
结论:
- 建立了乳酸细菌和酵母菌的"培养前选"策略,推进益生菌资源开发.
- 开发的方法在发酵食品中的益生菌初步查率上有了显著的改善.
- 从复杂的环境样本中提取益生菌资源提供了强大的技术支持.
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