快速和绿色的细菌分类方法使用机器学习和NIR光谱学
Leovergildo R Farias1, João Dos S Panero1, Jordana S P Riss2
1Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
使用近红外光谱 (NIR) 和机器学习 (ML) 应用了绿色化学原理,以快速识别细菌. 这种可持续的方法实现了百分之百的准确性,将细菌分为格拉姆阳性和格拉姆阴性组.
科学领域:
- 分析化学 分析化学
- 绿色化学 绿色化学
- 生物技术是生物技术.
背景情况:
- 绿色化学对于控制污染和实现可持续发展目标至关重要.
- 近红外光谱 (NIR) 为分子识别提供了更快,更具成本效益的替代方案.
- 准确的细菌识别在各种科学和工业应用中至关重要.
研究的目的:
- 开发一种快速和绿色的方法来识别和分类使用NIR光谱和ML的细菌.
- 为了区分格拉姆阴性和格拉姆阳性细菌.
- 将分析方法与可持续发展和绿色分析化学原则保持一致.
主要方法:
- 使用近红外扩散反射光谱与扩散反射配件相结合.
- 采用机器学习 (ML) 算法,包括主要组件分析 (PCA),层次集群分析 (HCA) 和K-最近邻居 (KNN).
- 开发了用于识别和分类大肠杆菌,沙门氏菌,菌和单细胞菌的模型.
主要成果:
- 在识别和分类四种目标细菌方面取得了100%的准确性.
- 成功地将细菌分为格拉姆阴性和格拉姆阳性群体,准确度很高.
- 证明了将NIR光谱与ML结合用于细菌分析的有效性.
结论:
- 开发的NIR光谱和ML方法为细菌识别和分类提供了高度准确和高效的方法.
- 这种绿色和快速的分析方法支持全球可持续发展政策和绿色分析化学.
- 这项研究表明,该研究在细菌分析和诊断中具有常规应用的巨大潜力.
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