机器学习辅助聚合诱导的基于纳米的发射式传感器阵列,用于对多种食源性病原体的临床识别
Yuechun Li1, Zhaowen Cui1, Ziqi Wang1
1College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China.
Analytical chemistry
|April 15, 2024
概括
使用新型纳米传感器阵列快速识别食源性病原体. 这项技术与机器学习相结合,在一小时内实现了高精度的多种病原体检测,提高了食品安全.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
背景情况:
- 及时识别食源性病原体对于有效的食品安全管理和预防疫情至关重要.
- 目前用于病原体检测的方法可能耗时,延迟对食品安全事件的反应.
研究的目的:
- 开发一个快速和同时的病原体检测系统,使用一个新的纳米传感器阵列.
- 评估纳米传感器阵列在识别和量化各种食物传播病原体方面的性能.
主要方法:
- 四个聚合诱导的发射纳米的合成,具有不同的表面特性.
- 开发一个纳米传感器阵列,利用这些纳米进行病原体相互作用.
- 机器学习算法的应用用于数据分析和病原体歧视.
主要成果:
- 纳米传感器阵列成功地在1小时内识别了8种不同的病原体,准确度为93.75-100%.
- 定量分析表明,可以区分*Cronobacter sakazakii*和*Listeria monocytogenes*的度,分别为10^3和10^2 CFU mL^-1.
- 该系统在1 × 10^4 CFU mL^-1的牛奶样本中准确识别了8种病原体,证明了其实际适用性.
结论:
- 开发的纳米传感器阵列为同时检测病原体提供了一个高度准确和快速的方法.
- 这项技术显示了实时监测食品危害和改善食品安全协议的巨大潜力.
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