表面增强拉曼光谱法对食源性致病菌的鉴别

Identification of Foodborne Pathogen Using Surface-Enhanced Raman Spectroscopy

表面增强拉曼光谱(SERS)以其简单、快速、高灵敏度等优点在食源性致病菌检测应用领域备受关注.本文基于SERS的指纹光谱优势,结合数学统计方法,实现了对食品中3种常见致病菌(大肠杆菌、金黄色葡萄球菌、粪肠球菌)的鉴别分析.研究中考察了SERS基底与细菌的不同结合方式,即带负电银纳米粒子直接与细菌混合吸附(Ag NPs^(-)-细菌)、带正电银纳米粒子直接与细菌混合吸附(Ag NPs^(+)-细菌)、在细菌表面原位生长银纳米粒子(细菌@AgNPs),以及分散溶剂对SERS光谱结果的影响.随后,基于获得的3种细菌的SERS指纹光谱,比较了多种数学统计方法的分类效果.结果表明,通过层次聚类分析、主成分分析和正交偏最小二乘判别分析法,均能对3种食源性致病菌进行区分,为利用SERS技术鉴别食源性致病菌提供了技术借鉴.

Surface-enhanced Raman spectroscopy(SERS)has attracted increasing interest due to its simple operation,rapid response,and high sensitivity.In this study,using the SERS spectra of bacteria that provide rich characteristic information,three pathogenic bacteria(Escherichia coli,Staphylococcus aureus and Enterococcus faecalis)were detected and identified through mathematical statistical methods.The fingerprint spectra of the three bacteria were obtained by optimizing the combination of SERS substrates,e.g.,electrostatically binding to negatively charged AgNPs(AgNPs^(-)-bacteria),electrostatically binding to positively charged AgNPs(AgNPs^(+)-bacteria)and in situ growth of AgNPs on bacteria(bacteria@AgNPs),as well as dispersion solvents.Based on the results of Raman fingerprint spectra of the three bacteria,a variety of mathematical and statistical methods were explored.The results showed that the hierarchical clustering analysis,principal component analysis,and orthogonal partial least squares discriminant analysis are capable of classifying the fingerprint spectra of the three foodborne pathogens.This study provided a potential method for identifying foodborne pathogens using the SERS technology.