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在食品矩阵上监测致病细菌,使用支持机器学习的纸质染色数组

Zhen Jia¹, Yaguang Luo², Dayang Wang³

¹Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, 32611, USA.

Biosensors & bioelectronics

|January 6, 2024

概括

这项研究增强了微生物食品安全的纸质色素阵列传感器 - 机器学习 (PCA-ML) 方法. 改进的PCA-ML方法允许快速,非接触检测多种食物传播病原体,并具有高准确性.

科学领域:

食品科学食品科学食品科学
微生物学微生物学
分析化学分析化学

背景情况:

目前微生物食品安全监测面临的挑战包括繁的采样和无法同时检测多种病原体.
病原体排放的挥发性有机化合物 (VOC) 是一种潜在的非破坏性检测方法.
现有的传感器技术需要针对灵敏度,特异性和易用性进行优化.

研究的目的:

显著改进纸质染色阵列传感器 - 机器学习 (PCA-ML) 微生物食品安全方法.
为了使多种食物传播病原体的非接触,时间依赖和同时监测.
建立基于VOC的病原体检测的理论和实践框架.

主要方法:

精简染料选择,传感器制造,数据库构建和PCA-ML系统的机器学习算法开发.
使用特定病原体发射的VOC进行检测.
在准确性和灵敏性方面验证了PCA-ML方法.

主要成果:

实现了对Listeria monocytogenes,沙门氏菌和大肠杆菌O157:H7.7的非接触,同时监测.
在低至1 log CFU/g的水平上检测到病原体,准确度超过90%.
证明色素反应取决于VOC度的时间积分.

结论:

增强的PCA-ML方法为微生物食品安全监测提供了重大进展.
这种方法可以适应各种食物矩阵和病原体,使物种和菌株特定的识别成为可能.
这项技术在整个食品供应链中具有强大的实施潜力.

关键词:

检测检测检测检测检测机器学习是机器学习.纸质染色阵列传感器的传感器数组.致病性细菌是一种致病性细菌.

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