{"title":"Identification and Quantification of Multiple Pathogenic Escherichia coli Strains Based on a Plasmonic Sensor Array","authors":"Yang Zhang, Chuping Zhao, Kaiyi Zheng, Haoran Li, Tianxi Yang, Feng Hu, Junjun Zhang, Xiaowei Huang, Zhihua Li, Jiyong Shi, Zhiming Guo, Shipeng Gao, Xiaobo Zou","doi":"10.1021/acs.analchem.5c00240","DOIUrl":null,"url":null,"abstract":"Pathogenic <i>Escherichia coli</i> (<i>E. coli</i>) is a widespread and clinically significant foodborne pathogen. Due to its high mutation rates and phenotypic diversity, rapid identification of its subtypes remains challenging and prone to false positives when detecting single strains. In this study, we developed a plasmonic sensor array with high-dimensional signal readouts (ζ-potential, dynamic light-scattering (DLS), surface-enhanced Raman scattering (SERS), and ultraviolet–visible (UV–vis) absorption spectra) for the selective discrimination of pathogenic <i>E. coli</i>, integrated with bacterial culture methods. The plasmonic sensor units demonstrated strong encoding capabilities, facilitating the differentiation of subtle variations among various <i>E. coli</i> strains and showing excellent anti-interference performance. The array realized different pathogenic <i>E. coli</i> strains, bacterial mixture identification, and even quantitative detection. Remarkably, the working concentration for the sensor array was notably low, at 10<sup>4</sup> CFU/mL. Finally, by incorporating bacterial isolation culture, the designed sensor array obtained 100% accuracy in detecting <i>E. coli</i> in real food samples. These findings highlight the sensor array’s potential for applications in food safety monitoring and clinical diagnostics, offering a sensitive, rapid, and reliable tool for pathogen detection in complex samples.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"28 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.analchem.5c00240","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Pathogenic Escherichia coli (E. coli) is a widespread and clinically significant foodborne pathogen. Due to its high mutation rates and phenotypic diversity, rapid identification of its subtypes remains challenging and prone to false positives when detecting single strains. In this study, we developed a plasmonic sensor array with high-dimensional signal readouts (ζ-potential, dynamic light-scattering (DLS), surface-enhanced Raman scattering (SERS), and ultraviolet–visible (UV–vis) absorption spectra) for the selective discrimination of pathogenic E. coli, integrated with bacterial culture methods. The plasmonic sensor units demonstrated strong encoding capabilities, facilitating the differentiation of subtle variations among various E. coli strains and showing excellent anti-interference performance. The array realized different pathogenic E. coli strains, bacterial mixture identification, and even quantitative detection. Remarkably, the working concentration for the sensor array was notably low, at 104 CFU/mL. Finally, by incorporating bacterial isolation culture, the designed sensor array obtained 100% accuracy in detecting E. coli in real food samples. These findings highlight the sensor array’s potential for applications in food safety monitoring and clinical diagnostics, offering a sensitive, rapid, and reliable tool for pathogen detection in complex samples.
期刊介绍:
Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.