深度学习辅助光单颗粒检测Fumonisin B1由热驱动催化和Argonaute驱动
Xianfeng Lin1,2, Lixin Kang1,2, Jiaqi Feng1,2
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China.
Analytical chemistry
|January 27, 2025
概括
一种新的深度学习和驱动的催化方法使得在食品中超敏感检测到烟素B1 (FB1). 这种光单颗粒感应平台可提高食品安全监测的精度和效率.
科学领域:
- 生物传感和分析化学
- 食品安全 食品安全
- 生物技术是生物技术.
背景情况:
- 准确检测微量真菌毒素,如烟素B1 (FB1) 对于食品安全和公共卫生至关重要.
- 现有的方法可能缺乏快速选农产品和食品所需的灵敏度或效率.
研究的目的:
- 开发一种超敏感的光单颗粒感应平台,用于检测烟素B1 (FB1).
- 整合深度学习,以提高真菌毒素检测中的分析效率.
主要方法:
- 设计了一种新的平台,将驱动催化 (EDC) 和阿尔戈诺特 (Ago) 蛋白与阿普坦体结合起来.
- 用生物素修改的单链DNA和红色光编码的微球被用作信号探针.
- 采用YOLOv9深度学习模型,快速准确地计算磁分离后的光粒子.
主要成果:
- 开发的平台实现了FB1.1的超敏感检测极限 (LOD) 0.89 pg/mL.
- 建立了一个广泛的线性检测范围,从1 pg/mL到100 ng/mL.
- 在真实食品样本中,令人满意的恢复率 (87.2-113.5%) 证明了该方法的实用性.
结论:
- 整合aptamer,EDC和Argonaute扩大了生物传感能力并提高了检测灵敏度.
- 深度学习显著提高了光单粒子检测的分析效率.
- 这种方法为食品安全监测中敏感和高效的真菌毒素检测提供了一个有希望的策略.
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