背景(考古学)
计算机科学
生化工程
生物技术
风险分析(工程)
计算生物学
生物
医学
工程类
古生物学
作者
Assia Mairi,Nasir A. Ibrahim,Takfarinas Idres,Abdelaziz Touati
出处
期刊:Toxins
[MDPI AG]
日期:2025-06-23
卷期号:17 (7): 319-319
被引量:1
标识
DOI:10.3390/toxins17070319
摘要
Staphylococcus aureus is a leading cause of foodborne intoxication globally, driven by its heat-stable enterotoxins (SEs), which pose significant public health risks. This review critically evaluates modern and traditional methodologies for detecting S. aureus and its enterotoxins in food matrices, emphasizing their principles, applications, and limitations. The review includes a dedicated section on sample preparation and pretreatment methods for diverse food substrates, addressing a critical gap in practical applications. Immunological techniques, including ELISA and lateral flow assays, offer rapid on-site screening but face matrix interference and variable sensitivity challenges. Molecular methods, such as PCR and isothermal amplification, provide high specificity and speed for bacterial and toxin gene detection but cannot confirm functional toxin production. Sequencing-based approaches (e.g., WGS and MLST) deliver unparalleled genetic resolution for outbreak tracing but require advanced infrastructure. Emerging biosensor technologies leverage nanomaterials and biorecognition elements for ultra-sensitive real-time detection, although scalability and matrix effects remain hurdles. Mass spectrometry (MALDI-TOF MS) ensures rapid species identification but depends on pre-isolated colonies. Traditional microbiological methods, while foundational, lack the precision and speed of molecular alternatives. The review underscores the necessity of context-driven method selection, balancing speed, sensitivity, and resource availability. Innovations in multiplexing, automation, AI-based methods, and integration of complementary techniques are highlighted as pivotal for advancing food safety surveillance. Standardized validation protocols and improved reporting of performance metrics are urgently needed to enhance cross-method comparability and reliability in outbreak settings.
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