Suppr超能文献

基于 Ag@AuNP 阵列基底的表面增强拉曼散射法对熟肉制品中细菌芽孢的特征物质分析及快速检测。

Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate.

机构信息

College of Food Science and Technology, Henan Agricultural University, Zhengzhou, 450002, PR China; International Joint Laboratory of Meat Processing and Safety in Henan Province, Henan Agricultural University, Zhengzhou, 450002, PR China.

College of Food Science and Technology, Henan Agricultural University, Zhengzhou, 450002, PR China; International Joint Laboratory of Meat Processing and Safety in Henan Province, Henan Agricultural University, Zhengzhou, 450002, PR China.

出版信息

Anal Chim Acta. 2024 Jun 15;1308:342616. doi: 10.1016/j.aca.2024.342616. Epub 2024 Apr 16.

Abstract

BACKGROUND

Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety.

RESULTS

In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 10). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL.

SIGNIFICANCE

It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products.

摘要

背景

细菌孢子是中高温灭菌肉制品中主要的潜在危害因素,其发芽及随后的繁殖和代谢可导致食品变质。此外,一些物种的孢子对消费者的健康和安全构成威胁。快速检测、预防和控制细菌孢子一直是科学问题,也是中高温肉类行业的主要挑战。在肉类产品中早期、敏感地识别孢子是保障消费者健康和安全的决定性因素。

结果

本研究采用两步合成法和液-界面自组装法,开发了一种新型、稳定的 Ag@AuNP 阵列基底,可直接检测实际肉品样品中的细菌孢子,无需额外的体外细菌培养。结果表明,Ag@AuNP 阵列基底具有较高的重现性和拉曼增强效果(1.35×10)。五种细菌孢子的表面增强拉曼散射(SERS)光谱的差异主要来源于孢子衣和内膜中的蛋白质、皮层的肽聚糖以及孢子核心内的 Ca⁺-DPA。肉品基质中线性判别分析对孢子的正确识别率可达 100%。SERS 定量模型的平均回收率精度约为 101.77%,检测限可达到 10 CFU/mL 以下。

意义

为肉品中孢子的特征物质分析和及时监测提供了有前景的技术策略。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验