食品中克罗诺杆菌的检测方法研究进展

Research progress on detection methods for Cronobacter in foods

中文关键词:  克罗诺杆菌  生化检测  酶联免疫吸附测定  基质辅助激光解吸电离飞行时间质谱  纳米传感器  人工智能

英文关键词:Cronobacter  biochemical detection  enzyme-linked immunosorbent assay  matrix-assisted laser desorption/ionization time-of-flight mass spectrometry  nanosensor  artificial intelligence

中文摘要:

      克罗诺杆菌是一种常见的致病菌, 可引起新生儿和婴儿脑膜炎、败血症和坏死性小肠结肠炎, 严重者可引起神经系统后遗症, 严重威胁婴幼儿食品安全与公众健康, 因此建立高效、准确的检测方法具有重要意义。本文系统综述了食品中克罗诺杆菌检测技术的研究进展, 并按照“培养确证-快速筛查-活菌定量-现场检测-智能解析”的层级逻辑进行归纳。传统培养法仍是获得活菌分离株、结果确证及后续溯源分析的基础; 免疫富集、实时荧光定量聚合酶链式反应/数字聚合酶链式反应及等温扩增技术提高了低丰度样品的筛查效率和定量能力; 基质辅助激光解吸电离飞行时间质谱法、代谢组学、表面增强拉曼光谱和流式细胞术拓展了快速鉴定、表型解析和细胞状态评价的应用边界; 纳米材料、微流控芯片、生物传感器及便携式平台进一步促进了检测方法的集成化、现场化和高灵敏化。此外, 人工智能在基因组溯源、传感信号解析、图像识别及多源数据融合中的应用, 为检测结果的智能判读提供了新思路。本文重点分析各类方法的互补关系、适用场景及转化瓶颈, 为食品安全监管提供更强有力的技术支撑。

英文摘要:

      Cronobacter are common pathogenic bacteria that can cause meningitis, septicemia and necrotizing enterocolitis in neonates and infants. Severe infections may lead to neurological sequelae, posing a serious threat to infant food safety and public health. Therefore, the establishment of efficient and accurate detection methods is great significance. This review systematically summarized recent advances in the detection technologies for Cronobacter in foods and organized them according to a hierarchical framework of “culture-based confirmation-rapid screening-viable-cell quantification-on-site detection-intelligent interpretation”. Conventional culture-based methods remain fundamental for obtaining viable isolates, confirming results and supporting subsequent source-tracing analysis. Immunoenrichment, real-time fluorescent quantitative polymerase chain reaction/digital polymerase chain reaction and isothermal amplification technologies had improved the screening efficiency and quantitative capability for low-abundance samples. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, metabolomics, surface-enhanced Raman spectroscopy and flow cytometry had expanded the application boundaries of rapid identification, phenotypic characterization and cellular state evaluation. Nanomaterials, microfluidic chips, biosensors and portable platforms had further promoted the integration, field applicability and high sensitivity of detection methods. In addition, the application of artificial intelligence in genomic source tracing, sensor signal analysis, image recognition and multi-source data fusion provides new perspectives for the intelligent interpretation of detection results. This review focused on the complementary relationships, applicable scenarios and translational bottlenecks of different detection methods, thereby providing stronger technical support for food safety supervision.