使用开放式深度学习驱动的单细胞拉曼光谱学精确识别活体细菌的生长阶段
Yufeng Yuan1, Yifan Sun2, Fusheng Du1
1School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, Guangdong, China.
Analytical chemistry
|March 13, 2026
概括
这项研究引入了一种先进的深度学习方法,使用单细胞拉曼光谱来识别细菌生长阶段. 新的开放式模型准确地区分了菌的生长阶段,甚至是未知的物种,这对食品安全和疾病控制至关重要.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 在单细胞水平上识别细菌生长阶段是具有挑战性的,因为细胞异质性.
- 准确的生长阶段确定对于发酵,食品安全和传染病控制至关重要.
研究的目的:
- 开发和验证一种开放式的深度学习策略,用于使用单细胞拉曼光谱识别细菌生长阶段.
- 通过各种生长阶段精确追踪单个菌细胞/子.
主要方法:
- 集成卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型进行深度学习分析.
- 使用插值算法增强的光谱转移策略增强拉曼光谱数据.
- 开发一个开放式的深度学习配置,使用增强的Softmax模块进行强大的分类.
主要成果:
- 在一个封闭的环境中,在13个增长时间点中,实现了96.52 ± 0.88%的预测准确度.
- LSTM分析显示,在12-20小时之间,生理状态发生了显著的变化.
- 对于训练有素,未见过的和未知的Bacillus物种,证明了高的开放式准确性 (92.15 ± 1.67%).
结论:
- 开放式深度学习驱动的单细胞拉曼光谱为识别复杂环境中的细菌生长阶段提供了强大的工具.
- 该方法对食品安全,发酵优化和病原体动力学研究的应用有希望.
- 这种方法有效地区分已知的,新鲜的和未知的细菌物种及其生长阶段.
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