由人工智能支持的生物感应用于快速检测病原体:从液态食品到农业用水
Jiyoon Yi1, Nicharee Wisuthiphaet2, Pranav Raja3
1Department of Biological & Agricultural Engineering, University of California, Davis, CA 95616, United States of America; Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI 48824, United States of America.
Water research
|June 30, 2023
概括
这项研究引入了一个AI-biosensing框架,用于快速,自动检测水中的病原体. 人工智能模型准确地识别食品和农业用水中的细菌,提高了安全性.
科学领域:
- 微生物学 微生物学
- 人工智能的人工智能
- 生物感应是一种生物感应.
背景情况:
- 快速检测病原体对于食品安全和公共卫生至关重要.
- 环境样本通常含有复杂的矩阵,阻碍病原体识别.
- 目前的方法需要专门的人员,并且可能耗费大量时间.
研究的目的:
- 开发一个加速和自动化的AI-biosensing框架用于病原体检测.
- 识别和量化各种水样中的细菌,包括液态食品和农业用水.
- 在病原体分析中克服复杂的环境背景所带来的挑战.
主要方法:
- 利用深度学习模型,对来自菌体相互作用的微观细菌模式进行训练.
- 在培训期间使用增强数据集,以提高数据效率.
- 在混合文化上微调模型,并对现实世界水样和环境噪音进行推断.
主要成果:
- 人工智能模型在不到5.5小时内实现了快速预测.
- 在现实世界水样中检测病原体的准确性达到了80-100%.
- 展示了模型对未见的环境数据进行概括的能力.
结论:
- 人工智能生物感知框架使得有效和自动的病原体检测成为可能.
- 突出了微生物水质监测在食品和农业的潜在应用.
- 验证了模型在处理复杂,杂的环境样本时的强度.
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