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通过氧化还原潜力方法识别环境污染物和致病细菌

Eya Yakdhane¹, Dóra Tőzsér², Oktay Haykir¹

¹Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), Budapest 1118, Hungary.

|July 18, 2024

概括

快速检测微生物活动,使用氧化还原潜力测量,比传统方法快得多. 这种技术通过分析独特的动态模式和曲线形状来区分细菌菌株,以进行准确的分类.

科学领域:

微生物学微生物学
生物技术是生物技术.
分析化学分析化学

背景情况:

传统的微生物培养方法耗时,需要更快的替代品.
氧化还原潜力的测量提供了一种快速方法,通过监测介质中的氧化变化来检测微生物活动.
这种方法表明氧气和营养消耗以及减少物质的产生.

研究的目的:

评估氧化还原潜力测量作为微生物检测和分化的快速方法.
评估数学模型和机器学习算法对细菌菌株的分类的准确性,以氧化还原潜力动力学为基础.
建立一个标准化的预处理方法,用于氧化还原潜力数据.

主要方法:

测量氧化还原潜力以检测微生物活动,在16小时内获得结果.
对细菌运动模式和氧化还原潜力曲线形状进行细菌菌株分化的分析.
描述参数,戈珀茨函数和支持向量机 (SVM) 的应用用于分类.
基于曲点时间的数据标准化,以考虑度依赖的检测时间 (TTD).

主要成果:

氧化还原潜能测量检测到微生物活动显著更快 (16小时) 比参考板计数技术 (≥24小时).
描述性参数达到98.2%的准确性,而Gompertz模型在分类六种细菌物种时达到91.6%的准确性.
在细菌菌株的分类中,SVM证明了其基于其独特的氧化还原潜力动态概况的有效性.

结论:

氧化还原潜力的测量是一种快速有效的微生物检测方法.
数学建模 (Gompertz函数) 和描述参数,结合SVM,可以准确地区分细菌菌株.
对氧化还原潜力数据的标准化对于可靠的分类至关重要,尤其是在处理不同微生物度时.

关键词:

细菌菌株的分类基于氧化还原潜力曲线.埃斯克里希亚病 (Escherichia) 是一种疾病.李斯特里亚病毒 (Listeria) 是一种微生物生长模型多变量分类是多变量分类.伪omonas 是一个类型的.沙门氏菌是一种沙门氏菌.

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