深度学习在数字微流体芯片中增强了可活性食品传播病原体的多重检测
Han Quan1, Siyuan Wang1, Xinge Xi2
1Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China.
Biosensors & bioelectronics
|November 24, 2023
概括
一个新的深度学习数字微流体平台快速检测可行的食物传播病原体. 这种方法使用时间间隔成像和人工智能,在短短7小时内准确地进行细菌类型和量化.
科学领域:
- 微流体学 微流体学
- 深度学习 (Deep Learning) 是一种深度学习.
- 检测食物传播病原体的检测
背景情况:
- 培养是量化食品传播病原体的标准,但速度缓慢且劳动密集.
- 需要快速,准确和易于使用的方法来检测病原体.
研究的目的:
- 开发一个深度学习增强的数字微流体平台,用于多重检测可行的食品传播病原体.
- 提高细菌类型和量化的速度和准确性.
主要方法:
- 使用一个时间缩短图像驱动的EfficientNet-Transformer网络 (TLENTNet) 进行细菌分析.
- 采用一个微流体芯片和800微,用于单个细菌封装.
- 每15分钟拍摄一次细菌生长的时差图像,用于时空特征分析.
主要成果:
- 在检测四种细菌物种 (S. typhimurium,E. coli O157:H7,S. aureus,B. cereus) 中获得了97.72%的平均准确性.
- 证明了63 CFU/mL的检测极限.
- 在7小时内完成检测和量化,比传统方法快得多.
结论:
- 开发的基于深度学习的数字微流体平台为食品传播病原体检测提供了快速而准确的替代品.
- 这项技术有可能加强食品安全监测和诊断.
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