使用MALDI-TOF MS与人工智能集成的Vibrio harveyi clade的直接检测和差异化,以有效管理疫情
Eiseul Kim1, Dabin Kim1, Yinhua Cai1
1Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
Food chemistry
|July 15, 2025
概括
这项研究引入了一种人工智能驱动的MALDI-TOF MS方法,用于精确识别Vibrio harveyi clade物种. 随机森林模型实现了完美的准确性,在海鲜安全和水产养殖疾病控制方面表现优于商业数据库.
科学领域:
- 微生物学 微生物学
- 分析化学 分析化学
- 生物信息学是一种生物信息学.
背景情况:
- 准确识别Vibrio harveyi类物种对于海鲜安全和水产养殖疾病管理至关重要.
- 目前的识别方法在分类性能方面存在局限性.
研究的目的:
- 开发一种人工智能辅助的矩阵辅助激光消耗离子化-飞行质谱时间 (MALDI-TOF MS) 方法,用于直接的殖民地分析.
- 提高V. harveyi类物种识别的准确性和效率.
主要方法:
- 用MALDI-TOF MS.进行直接殖民地分析,利用盘上蛋白质提取.
- 评估了六个算法,包括一个随机森林模型,用于物种分类.
- 采用主要成分分析 (PCA) 进行特征选择和物种分离.
主要成果:
- 随机森林模型在分类V. harveyi类物种方面达到100%的准确性,超过商业数据库 (79.57%的准确性).
- 在PCA分析中,通过使用模型选择的前10%特征,证明了增强的物种分离.
- 确定了7194.7,3609.8和9179.6m/z的关键区分质量峰值.
结论:
- 开发的人工智能辅助的MALDI-TOF MS方法为V. harveyi clade物种识别提供了一个快速,数据库独立的解决方案.
- 这种方法显著提高了MALDI-TOF MS在食品安全和水产养殖业常规质量控制方面的实用性.
- 该方法显示出稳定性和通用性,为微生物识别提供了有价值的工具.
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