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基于SERS技术的食源性致病菌芽孢拉曼光谱特征结构分析及快速识别
引用本文:刘世杰,朱瑶迪,李苗云,赵改名,赵莉君,马阳阳,王 娜.基于SERS技术的食源性致病菌芽孢拉曼光谱特征结构分析及快速识别[J].光谱学与光谱分析,2022,42(9):2774-2780.
作者姓名:刘世杰  朱瑶迪  李苗云  赵改名  赵莉君  马阳阳  王 娜
作者单位:1. 河南农业大学食品科学技术学院,河南 郑州 450000
2. 河南省肉品加工与安全国际联合实验室,河南 郑州 450000
基金项目:国家“十三五”重点研发计划项目(2019YF1605700),河南省杰青项目(212300410008),基于银钠米种子介导二次生长自组装SERS基底关键技术研究及应用(22A550013),河南省重点研发与推广专项(科技攻关)项目(202102110141),国家自然科学基金项目(31801671),国家现代农业(肉牛牦牛)产业技术体系建设专项(CARS-37),河南农业大学科技创新基金项目(KJCX2020A17)资助
摘    要:为了探究食源性致病菌芽孢的拉曼特征指纹图谱,实现快速识别,该研究以产气荚膜梭菌(C. perfringens)、艰难梭菌(C. difficile)和蜡样芽孢杆菌(B. cereus)的芽孢为研究对象,以柠檬酸钠还原法制备的AgNPs溶胶为基底材料,用SERS技术对芽孢进行拉曼光谱检测,解析食源性致病菌芽孢的分子结构、不同芽孢之间的异同之处。将3种食源性致病菌芽孢的SERS光谱与主成分分析(PCA)和系统聚类分析(HCA)相结合并进行对比分析,实现不同种属食源性致病菌芽孢的定性识别。结果表明,不同食源性致病菌芽孢的SERS光谱的特异性和重现性良好。芽孢光谱中Ca2+-DPA的拉曼振动峰数量和峰强度占主要地位,其拉曼振动峰位置在657~663,818~820,1 017,1 389~1 393,1 441~1 449和1 572~1 576 cm-1波段。C. difficile spores SERS光谱中Ca2+-DPA的六个特征峰峰强度均高于C. perfringens sporesB. cereus sporesC. perfringens spores次之。Ca2+-DPA在1 017 cm-1(Ca2+-DPA)处拉曼峰强度在3种芽孢的SERS光谱中均最高且差异明显,是Ca2+-DPA的主要特征峰,也是3种芽孢的主要特征峰。此外,C. perfringens spores在936 cm-1(磷脂N—C拉伸)、1 294 cm-1(脂质中的CH2变形振动)、1 609 cm-1(蛋白质中的酪氨酸)和1 649 cm-1(蛋白质中的酰胺I)显示特有拉曼振动峰;C. difficile spores在890 cm-1(═COC═拉伸)显示特有拉曼振动峰。PCA分析结果显示PC1和PC2方差贡献率分别为51.1%和39.7%,累积贡献率达90.8%,可以将所有样本有效区分。HCA分析可以看出3种芽孢的SERS光谱被分为三个聚类,3种芽孢各自聚类无交叉干扰。结合多元统计分析不仅有效实现了3种芽孢之间的区分,也实现了梭菌属芽孢和杆菌属芽孢的区分,为食品安全控制提供有效手段。

关 键 词:食源性致病菌芽孢  表面增强拉曼光谱  AgNPs  光谱解析  快速识别  
收稿时间:2021-07-17

Raman Spectroscopic Characteristic Structure Analysis and Rapid Identification of Food-Borne Pathogen Spores Based on SERS Technology
LIU Shi-jie,ZHU Yao-di,LI Miao-yun,ZHAO Gai-ming,ZHAO Li-jun,MA Yang-yang,WANG Na.Raman Spectroscopic Characteristic Structure Analysis and Rapid Identification of Food-Borne Pathogen Spores Based on SERS Technology[J].Spectroscopy and Spectral Analysis,2022,42(9):2774-2780.
Authors:LIU Shi-jie  ZHU Yao-di  LI Miao-yun  ZHAO Gai-ming  ZHAO Li-jun  MA Yang-yang  WANG Na
Institution:1. College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450000, China 2. International Joint Laboratory of Meat Processing and Safety in Henan Province, Zhengzhou 450000, China
Abstract:In order to explore the Raman fingerprint of food-borne pathogenic bacteria spores for rapid identification. In this study, the spores of C. perfringens, C. difficile and B. cereus were used as the research objects. The SERS technology of AgNPs synthesized by the sodium citrate reduction method was used to detect the Raman spectroscopy of food-borne pathogenic bacteria spores and analyze the similarities or differences among different spores. The SERS spectra of three kinds of food-borne pathogenic bacteria spores were combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) for comparative analysis to identify different species of food-borne pathogenic bacteria spores. The results showed that the SERS spectra of different food-borne pathogen spores had sufficient specificity and reproducibility. In the SERS spectra of spores, the number and intensity of Raman vibration peaks of Ca2+-DPA were dominant, and the Raman vibration peaks were located at 657~663, 818~820, 1 017, 1 389~1 393, 1 441~1 449 and 1 572~1 576 cm-1. The intensity of six characteristic peaks of Ca2+-DPA in SERS spectra of C. difficile spores were higher than that of C. perfringens spores and B. cereus spores, followed by C. perfringens spores. The Raman peak intensity of Ca2+-DPA at 1 017 cm-1 (Ca2+-DPA) of the three spores was the highest, and the difference was noticeable, which was the main characteristic peak of Ca2+-DPA and the main characteristic peak of the three spores. In addition, C. perfringens spores showed unique Raman peaks at 936 cm-1 (N—C stretching of phospholipid), 1 294 cm-1 (CH2 deformation vibration of lipid), 1 609 cm-1 (tyrosine of protein) and 1 649 cm-1 (amide Ⅰ of protein). C. difficile spores showed unique Raman peaks at 890 cm-1 (═COC═ stretching). PCA analysis showed that the variance contribution rates of PC1 and PC2 were 51.10% and 39.70%, respectively, and the cumulative contribution rate was 90.8%, which could effectively distinguish all samples. HCA analysis indicated that the SERS spectra of the three spores were divided into three clusters, and each cluster of the three spores had no cross-interference. The combination of multivariate statistical analysis effectively realized the distinction among the three spores and distinguished the distinction between Clostridium spores and Bacillus spores, providing an effective means for food safety control.
Keywords:Food-borne pathogenic bacteria spores  SERS  AgNPs  Spectral analysis  Rapid identification  
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