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基于MALDI-TOF MS蛋白质配置文件的Latilactobacillus sakei亚种的分类使用机器学习模型

Eiseul Kim¹, Seung-Min Yang¹, So-Yun Lee¹

¹Department of Food Science and Biotechnology, Institute of Life Sciences & Resources, Kyung Hee University, Yongin, South Korea.

Microbiology spectrum

|August 20, 2024

概括

机器学习与MALDI-TOF MS相结合,可以有效地区分Latilactobacillus sakei亚种,这对食品发酵和安全至关重要. 这种方法为微生物分类提供了一种快速,高分辨率的方法,性能优于传统的数据库.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
食品科学食品科学食品科学

背景情况:

在发酵食品中,Latilactobacillus sakei是至关重要的,亚种具有不同的作用 (发酵与破坏).
精确区分L. sakei亚种对于食品质量和安全至关重要.
飞行质谱的矩阵辅助激光脱吸/电离时间 (MALDI-TOF MS) 是微生物识别的标准,但缺乏亚种分辨率.

研究的目的:

开发一种使用MALDI-TOF MS和机器学习区分L. sakei亚种的新方法.
为了评估机器学习算法的高分辨率微生物分类的性能.
为食品工业提供一个具有成本效益的工具来监测L. sakei亚种.

主要方法:

收集了227个L. sakei菌株,并获得了908个MALDI-TOF MS光谱.
应用机器学习算法,包括部分最小平方-差异分析 (PLS-DA),主要组件分析-K-最近邻 (PCA-KNN),支持矢量机 (SVM) 和随机森林 (RF).
将机器学习模型的性能与用于分种识别的Biotyper数据库进行比较.

主要成果:

生物型数据库在亚种层面上只达到68.7%的准确性.
机器学习模型表现出高性能:PLS-DA (0.823),PCA-KNN (0.914),SVM (0.903). 机器学习模型表现出高性能:PLS-DA (0.823),PCA-KNN (0.914),SVM (0.903). 机器学习模型表现出高性能:PLS-DA (0.823),PCA-KNN (0.914),机器学习模型表现出高性能.
随机森林 (RF) 实现了最高准确率 (0.954) 和AUROC 0.99,明显超过其他方法.

结论:

机器学习与MALDI-TOF MS相结合,为L. sakei亚种的分类提供了强大的高分辨率方法.
这种方法显著改进了微生物亚种的传统识别方法.
开发的模型为食品工业中监测L. sakei提供了一个有希望的,高效的解决方案.

关键词:

拉提拉多巴西勒斯 (Latilactobacillus sakei) 是一个大麦子亚种.马尔迪-托夫 (MALDI-TOF) 是一个国家元首.这是分类分类的分类.发酵食品是发酵的食物.机器学习是机器学习.

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