随机概括模型学会全面检测与食物传播病原体相关的挥发性有机化合物通过拉曼光谱学
Bohong Zhang1, Anand K Nambisan1, Abhishek Prakash Hungund1
1Department of Electrical and Computer Engineering, Missouri University of Science and Technology Rolla Missouri 65409 USA bzdtx@mst.edu jieh@mst.edu.
RSC advances
|February 17, 2025
概括
本研究介绍了一种拉曼光谱和机器学习系统,用于通过挥发性有机化合物 (VOC) 检测食物传播的病原体. 这种创新方法在识别复杂混合物方面实现了高精度,提高了食品安全监测.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
背景情况:
- 食品安全依赖于检测病原体和污染物.
- 像培养这样的传统方法是缓慢而劳动密集的.
- 实时监控需要快速,准确的检测系统.
研究的目的:
- 开发一种结合拉曼光谱和机器学习的系统,用于检测挥发性有机化合物 (VOC).
- 在复杂的混合物中精确识别和量化与食品传播病原体相关的VOC.
- 为传统的食品安全测试方法提供快速的现场替代方案.
主要方法:
- 使用远程光纤拉曼探测器从42种不同的VOC混合物中收集光谱数据.
- 机器学习模型 (MLP,随机森林,XGBDT) 在1445个拉曼光谱上受过训练.
- 对稀释和未稀释的VOC样品进行了分类和回归分析.
主要成果:
- 机器学习模型在分类纯VOC时达到90%以上的准确性.
- 该系统可靠地识别了0.25%度 (400倍稀释) 的最多6种VOC的混合物.
- 回归模型准确地预测了VOC度降至1% (100倍稀释),R2>0.82.
结论:
- 合并的拉曼光谱和ML系统为食品安全提供了强大的工具.
- 这种方法可以快速,实时检测和分析食品传播病原体指标.
- 它为现场食品安全评估和质量控制提供了有价值的解决方案.
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