正电纳米银与卷积神经网络用于4种食源性致病菌的SERS鉴定  

Surface Enhanced Raman Scattering Detection of Four Foodborne Pathogens Using Positively Charged Silver Nanoparticles and Convolutional Neural Networks

作　　者：杨勇 董浩 王澍 桑瑶烁 李志刚 张龙 汪崇文 刘勇[1,2] Yang Yong;Dong Hao;Wang Shu;Sang Yaosuo;Li Zhigang;Zhang Long;Wang Chongwen;Liu Yong(Anhui Institute of Optics and Fine Mechanics,Hefei Institute of Physical Science,Chinese Academy of Sciences,Hefei 230031,Anhui,China;Science Island Branch,Graduate School of University of Science and Technology of China,Hefei 230026,Anhui,China;School of Life Sciences,Anhui Agricultural University,Hefei 230036,Anhui,China)

机构地区：[1]中国科学院合肥物质科学研究院安徽光学精密机械研究所,安徽合肥230031 [2]中国科学技术大学研究生院科学岛分院,安徽合肥230026 [3]安徽农业大学生命科学学院,安徽合肥230036

出　　处：《中国激光》2022年第15期187-196,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金面上项目(82072380);合肥物质科学研究院医疗器械监管专项(YZJJ2021-J-YQ4);安徽省自然科学基金(1908085QB85)。

摘　　要：提出一种联合表面增强拉曼散射(SERS)与卷积神经网络(CNN)的方法,并将其用于食源性致病菌的快速鉴定。以带正电荷的银纳米颗粒(AgNPs~+)为SERS基底,采集了金黄色葡萄球菌、大肠杆菌、副溶血性弧菌以及单增李斯特菌的SERS指纹谱,并在这些数据上训练了一个包含11个一维卷积层的残差网络ResNet11用于这4种病原菌SERS指纹谱的分类识别。实验结果表明:AgNPs^(+)是一种优秀的SERS增强基底,可在624 cm、730 cm等波段增强4种病原菌的主要拉曼峰;构建的ResNet11分类器对10^(3)mL^(-1)菌液分子浓度下采集的SERS指纹谱取得了99.30%的分类识别准确率,并且对10^(3)mL^(-1)菌液分子浓度下采集的SERS指纹谱取得98.00%的识别准确率。Objective Infectious diseases caused by foodborne pathogenic bacteria are always one of the most severe public health problems.Accurate detection of pathogenic microorganisms in food is necessary to guarantee food safety and to contain bacterial infection.Microbial culture-based methods and biochemical tests are still the golden standard in bacterial detection;however,these methods are time-consuming,taking about 2-3 days to carry out,and follow more than ten operation steps.In addition,new diagnostic technologies,such as conventional polymerase chain reaction,mass spectrometry,and DNA sequencing,suffer from many disadvantages including long processing time,laborious operation steps,limited sensitivity,and high cost;thus,they still cannot meet the requirements for clinical diagnosis and point-of-care testing.In recent years,bacterial detection methods based on surface enhanced Raman scattering(SERS)have achieved significant success and performed excellently on high-sensitivity,easy-to-operate,and fingerprint-based detection methods.In this paper,four major foodborne bacteria,namely,Staphylococcus aureus(S.aureus),Escherichia coli(E.coli),Vibrio parahaemolyticus(V.parahaemolyticus),and Listeria monocytogenes(L.monocytogenes),are used as research objects.Furthermore,a novel SERS method,which combines positively charged Ag nanoparticles(AgNPs^(+))and convolutional neural networks(CNN),is proposed in this paper for accurate and rapid detection of the above four bacteria.Methods Clinical isolates including 10 strains from each of S.aureus,E.coli,V.parahaemolyticus,and L.m onocytogenes are collected from the laboratory department of the Affiliated Hospital of Xuzhou Medical University.First,Ag NPsare prepared via reduction method of Na BH4 and are fabricated in a buffer solution as substrate for SERS.Then,Ag NPs@bacteria complexes are formed via electrostatic interactions,and high-quality SERS signals in a shift range o f 400-800 cmof pathogenic bacteria are measured from the forming complexes.Finally,a residual networ

关 键 词：生物光学 食源性致病菌 表面增强拉曼散射 带正电荷的银纳米颗粒 卷积神经网络 

分 类 号：O433[机械工程—光学工程]