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激光诱导分解光谱与机器学习相结合,用于快速量化大肠杆菌埃舍里奇亚菌度

Jingjing Wang¹, Jiahui Liang¹, Fei Chen¹

¹State Key Laboratory of Quantum Optics and Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, 030004, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030004, China.

|July 1, 2025

概括

这项研究引入了一种结合激光诱导分解光谱 (LIBS) 和机器学习的新方法,用于快速细菌量化. 该方法提供了准确和高效的微生物分析与最小的样本准备,对于食品安全和诊断至关重要.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

准确的细菌量化对于食品安全,环境监测和临床诊断至关重要.
细菌分析的传统方法往往耗时,复杂和昂贵.
需要快速,准确和具有成本效益的细菌量化技术.

研究的目的:

开发和验证一种新的方法,用于快速细菌度分析,使用激光诱导分解光谱 (LIBS) 与机器学习相结合.
优化LIBS参数,以提高细菌分析中的光谱质量.
为了评估不同机器学习算法的性能,用于细菌量化.

主要方法:

激光诱导分解光谱 (LIBS) 用于细菌分析.
关键的LIBS参数 (延迟时间,基质材料,激光重复率) 使用大肠杆菌 (大肠杆菌) 作为模型生物来优化.
支持向量回归 (SVR),梯度增强回归 (GBR) 和核回归 (KRR) 被评估为细菌度预测.

主要成果:

支持向量回归 (SVR) 模型实现了高的确定系数 (R2 = 0.99).
该SVR模型显示出较低的错误率 (RMSE = 7.3 × 105细胞/毫升,MAE = 4.2 × 105细胞/毫升).
方法验证证实了卓越的准确性 (恢复率100.03100.83%) 和精度 (RSD<2%).

结论:

开发的基于LIBS的机器学习方法为细菌量化提供了快速,准确和高效的技术.
这种方法有效地解决了光谱数据和细菌度之间的非线性关系.
该方法显示出在食品安全,环境监测和临床诊断方面的应用潜力很大,因为样本准备和快速分析的最小程度.

关键词:

细菌度的细菌度是什么埃舍里希亚大肠杆菌 (Escherichia coli) 是一个大肠杆菌.激光诱导的分解光谱学机器学习机器学习支持矢量回归的支持矢量回归

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