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根据ATR-FTIR光谱学识别沙门氏菌血清组,区分伤寒型和非伤寒型沙门氏菌

Maira Napoleoni¹, Stefano Ceschia², Elisa Mitri²

¹Centro di Riferimento Regionale Patogeni Enterici Marche, Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche "Togo Rosati", Via Maestri del Lavoro, 7, 62029 Tolentino, Macerata, Italy.

|November 27, 2024

概括

准确识别沙门氏菌血清组对于治疗食物传播疾病至关重要. 用机器学习的减弱总反射度-福里埃变换红外光谱 (ATR-FTIR) 实现了超过98%的准确性,识别了关键的Salmonella enterica血清组.

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:

在全球范围内,沙门氏菌病是一种重要的食物传播疾病,需要准确识别沙门氏菌株,以进行有效的治疗和跟踪.
传统的沙门氏菌血型鉴定复杂且耗时,影响了临床管理和流行病学监测.
区分伤寒和非伤寒型沙门氏菌株对于确定适当的抗生素治疗至关重要.

研究的目的:

为了评估减弱总反射率 - 里埃变换红外光谱 (ATR-FTIR) 光谱学与机器学习相结合的有效性,以快速识别沙门氏菌血清组.
评估该方法在区分主要的Salmonella enterica血清组 (B,C1,D1,E1) 的潜力,包括伤寒菌株.
确定这种方法是否可以作为缺乏传统血清定型设施的临床实验室的快速替代方案.

主要方法:

利用ATR-FTIR光谱学与机器学习算法相结合,用于直接分析固体单微生物沙门氏菌培养物.
使用I-dOne软件v2.2进行沙门氏菌物种检测和潜在的血清型表征.
经过培训并验证了使用248种沙门氏菌菌株的多变量分类模型.

主要成果:

该ATR-FTIR光谱和机器学习系统在识别四个关键的Salmonella enterica血清组时取得了>98%的整体准确性.
该方法在从固体培养物直接识别中表现出有效性,而不需要样品预处理.
综合系统显示了同时进行物种和血清组表征的潜力.

结论:

使用机器学习的ATR-FTIR光谱学提供了一种快速且高度准确的方法来识别Salmonella enterica血清组.
这种技术可以为病例管理和公共卫生当局提供重要的流行病学信息.
开发的系统为传统的血清定型方法提供了可行的替代方案,特别是对于资源有限的临床实验室.

关键词:

通过ATR-FTIR光谱学检测.我-做了一个.S. Typhiphi 是一个类型.沙门氏菌 spp. 沙门氏菌沙门氏菌 spp. 沙门氏菌血清组是血清组的组成部分.识别算法识别算法

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