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机器学习能够通过表面增强的拉曼光谱检测牙周病原体的多重检测

Rathnayake A C Rathnayake¹, Zhenghao Zhao², Nathan McLaughlin³

¹Department of Chemistry, Illinois Institute of Technology, Chicago, IL 60616, United States of America.

International journal of biological macromolecules

|December 14, 2023

概括

这项研究引入了一种新的,无标签的方法来检测牙周病原体,如Actinobacillus actinomycetemcomitans,使用表面增强的拉曼光谱和机器学习. 这种方法可以快速,准确和多重识别唾液中的口腔细菌.

科学领域:

微生物学微生物学
纳米技术纳米技术
频谱学是一种光谱学.

背景情况:

牙周炎是一种由细菌感染驱动的慢性炎症性疾病,导致牙支结构的破坏.
唾液微生物分析对于评估牙周健康至关重要.
现有的细菌检测方法通常是复杂的,需要标签.

研究的目的:

开发一种快速,无标签和多重检测方法,用于关键牙周病原体.
用原子力显微镜来描述牙周病原体的纳米尺寸.
建立一个基于机器学习的检测系统,使用表面增强的拉曼光谱.

主要方法:

原子力显微镜 (AFM) 用于确定Actinobacillus actinomycetemcomitans,Porphyromonas gingivalis和Streptococcus mutans的纳米尺寸.
用表面增强的拉曼光谱法 (SERS) 来获得每个细菌物种的独特,无标签的拉曼签名.
机器学习模型使用SERS光谱进行训练,以准确识别和量化纯样和混合样本中的细菌.

主要成果:

牙周病原体的特征是长度为0.6-1.1μm,宽度为500-700nm的纳米棒.
对每个目标病原体确定了不同的SERS光谱,使得无标签的检测成为可能.
一个机器学习模型在混合样本中同时识别所有三个测试病原体时达到95.6%的准确性.

结论:

与机器学习相结合,SERS提供了一个高度准确和高效的平台,用于无标签,多重检测牙周病原体.
这种方法显著简化了样品的准备,并加快了唾液中的细菌识别.
开发的传感方式有望在诊断和监测牙周炎方面广泛临床应用.

关键词:

没有标签的无标签.机器学习机器学习多重复合检测检测多重复合检测牙周病原体的病原体这就是 SERS SERS.

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