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纳米间隙辅助的SERS/PCR生物传感器合机器学习,用于食品中直接检测黄菌
1College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China.
Journal of agricultural and food chemistry
|January 3, 2025
概括
一种新型生物传感器使用表面增强的拉曼散射/聚合酶链反应 (SERS/PCR) 和机器学习检测牛奶中的金黄色葡萄球菌. 这种方法提供了一种敏感和特定的方法来识别这种食源性病原体.
科学领域:
- 食品安全 食品安全
- 生物传感器技术技术
- 分子诊断学 分子诊断
背景情况:
- 黄金葡萄球菌 (S. aureus) 对食品安全构成重大风险.
- 在牛奶等食品中准确和快速检测金黄色细菌至关重要.
研究的目的:
- 开发一种直接和特定的生物传感方法来检测牛奶中的金黄色细菌.
- 整合表面增强的拉曼散射/聚合酶链反应 (SERS/PCR) 与机器学习进行增强的检测.
主要方法:
- 使用PCR扩增S. aureus核基因.
- 通过使用双金属金和银纳米花 (Au/Ag FL@I-Mg2+) 的纳米间隙效应捕获和信号放大核基因.
- 使用机器学习算法分析SERS信号,特别是启动软收缩-部分最小平方.
主要成果:
- 开发的纳米间隙辅助SERS/PCR生物传感器使S. aureus的直接和特定感应成为可能.
- 机器学习模型实现了高性能,预测的根平均平方误差为0.437,预测集相关系数为0.967.
- 成功展示了一种新的无标签策略,用于检测金黄色细菌.
结论:
- 这项研究提出了一种有效的无标签策略,用于在牛奶中特定检测金黄色细菌.
- 生物传感器平台显示了通过修改特定的原料来适应检测其他食源性致病细菌的潜力.
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