Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods
The detection and classification of foodborne pathogenic bacteria is crucial for food safety monitoring, consequently requiring rapid, accurate and sensitive methods. In this study, the surface-enhanced Raman spectroscopy (SERS) technique coupled with chemometrics methods was used to detect and clas...
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MDPI AG
2024-11-01
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| Series: | Foods |
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| author | Huixin Zuo Yingying Sun Mingming Huang Stephanie Marie Fowler Jing Liu Yimin Zhang Yanwei Mao |
| author_facet | Huixin Zuo Yingying Sun Mingming Huang Stephanie Marie Fowler Jing Liu Yimin Zhang Yanwei Mao |
| author_sort | Huixin Zuo |
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| description | The detection and classification of foodborne pathogenic bacteria is crucial for food safety monitoring, consequently requiring rapid, accurate and sensitive methods. In this study, the surface-enhanced Raman spectroscopy (SERS) technique coupled with chemometrics methods was used to detect and classify six kinds of foodborne pathogenic bacteria, including <i>Salmonella typhimurium</i> (<i>S</i>. <i>typhimurium</i>), <i>Escherichia coli</i> (<i>E</i>. <i>coli</i>) O157:H7, <i>Staphylococcus aureus</i> (<i>S</i>. <i>aureus</i>), <i>Listeria monocytogenes</i> (<i>L</i>. <i>monocytogenes</i>), <i>Listeria innocua</i> (<i>L</i>. <i>innocua</i>), and <i>Listeria welshimeri</i> (<i>L</i>. <i>welshimeri</i>). First, silver nanoparticles (AgNPs) with different particle sizes were prepared as SERS-enhanced substrates by changing the concentration of sodium citrate, and the volume ratio of silver nanosol to bacterial solution was optimised to obtain the optimal SERS signal. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to classify the SERS spectra of six bacteria at three classification levels (Gram type level, genus level and species level), and appropriate classification models were established. Finally, these models were validated on 540 spectra using linear discriminant analysis (LDA), achieving an average accuracy of 95.65%. Overall, it was concluded that the SERS technique combined with chemometrics methods could achieve the rapid detection and classification identification of foodborne pathogenic bacteria, providing an effective means for food safety monitoring. |
| format | Article |
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| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-3f4da34e57c1495a86c3ab8879deb7732025-08-20T02:05:02ZengMDPI AGFoods2304-81582024-11-011322368810.3390/foods13223688Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric MethodsHuixin Zuo0Yingying Sun1Mingming Huang2Stephanie Marie Fowler3Jing Liu4Yimin Zhang5Yanwei Mao6College of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaCollege of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaCollege of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaNSW Department of Primary Industries, Centre for Red Meat and Sheep Development, P.O. Box 129, Cowra, NSW 2794, AustraliaCollege of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaCollege of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaCollege of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, ChinaThe detection and classification of foodborne pathogenic bacteria is crucial for food safety monitoring, consequently requiring rapid, accurate and sensitive methods. In this study, the surface-enhanced Raman spectroscopy (SERS) technique coupled with chemometrics methods was used to detect and classify six kinds of foodborne pathogenic bacteria, including <i>Salmonella typhimurium</i> (<i>S</i>. <i>typhimurium</i>), <i>Escherichia coli</i> (<i>E</i>. <i>coli</i>) O157:H7, <i>Staphylococcus aureus</i> (<i>S</i>. <i>aureus</i>), <i>Listeria monocytogenes</i> (<i>L</i>. <i>monocytogenes</i>), <i>Listeria innocua</i> (<i>L</i>. <i>innocua</i>), and <i>Listeria welshimeri</i> (<i>L</i>. <i>welshimeri</i>). First, silver nanoparticles (AgNPs) with different particle sizes were prepared as SERS-enhanced substrates by changing the concentration of sodium citrate, and the volume ratio of silver nanosol to bacterial solution was optimised to obtain the optimal SERS signal. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to classify the SERS spectra of six bacteria at three classification levels (Gram type level, genus level and species level), and appropriate classification models were established. Finally, these models were validated on 540 spectra using linear discriminant analysis (LDA), achieving an average accuracy of 95.65%. Overall, it was concluded that the SERS technique combined with chemometrics methods could achieve the rapid detection and classification identification of foodborne pathogenic bacteria, providing an effective means for food safety monitoring.https://www.mdpi.com/2304-8158/13/22/3688foodborne pathogenic bacteriasurface-enhanced Raman spectroscopysilver nanoparticleschemometricsclassification identificationbacterial typing |
| spellingShingle | Huixin Zuo Yingying Sun Mingming Huang Stephanie Marie Fowler Jing Liu Yimin Zhang Yanwei Mao Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods Foods foodborne pathogenic bacteria surface-enhanced Raman spectroscopy silver nanoparticles chemometrics classification identification bacterial typing |
| title | Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods |
| title_full | Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods |
| title_fullStr | Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods |
| title_full_unstemmed | Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods |
| title_short | Classification and Identification of Foodborne Bacteria in Beef by Utilising Surface-Enhanced Raman Spectroscopy Coupled with Chemometric Methods |
| title_sort | classification and identification of foodborne bacteria in beef by utilising surface enhanced raman spectroscopy coupled with chemometric methods |
| topic | foodborne pathogenic bacteria surface-enhanced Raman spectroscopy silver nanoparticles chemometrics classification identification bacterial typing |
| url | https://www.mdpi.com/2304-8158/13/22/3688 |
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