Boosting bacteria differentiation efficiency with multidimensional surface‐enhanced Raman scattering: the example of <i>Bacillus cereus</i>

Zhu, Chengye; Li, Wen; Wang, Dongmei; Gong, Zhengjun; Fan, Meikun

doi:10.1002/bio.4268

Cited by 8 publications

(6 citation statements)

References 57 publications

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“…One study modified AgNPs with multiple chemicals to provide additional dimensionality of the substrate. 77 The modified SERS substrate was able to distinguish different strains of B. cereus simply by PCA, while the bare AgNPs only separated S. aureus and B.…”

Section: Sers Substrate Modificationmentioning

confidence: 98%

“…The surface modification of SERS nanomaterials resulted in the additional difference of the SERS fingerprints to improve the differentiation efficiency. One study modified AgNPs with multiple chemicals to provide additional dimensionality of the substrate 77 . The modified SERS substrate was able to distinguish different strains of B. cereus simply by PCA, while the bare AgNPs only separated S. aureus and B. cereus .…”

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

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Recent advances of Raman spectroscopy for the analysis of bacteria

Rodriguez

Zhang

Wang

2023

Analytical Science Advances

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Rapid and sensitive bacteria detection and identification are becoming increasingly important for a wide range of areas including the control of food safety, the prevention of infectious diseases, and environmental monitoring. Raman spectroscopy is an emerging technology which provides comprehensive information for the analysis of bacteria in a short time and with high sensitivity. Raman spectroscopy offers many advantages including relatively simple operation, non‐destructive analysis, and information on molecular differences between bacteria species and strains. A variety of biochemical properties can be measured in a single spectrum. This short review covers the recent advancements and applications of Raman spectroscopy for bacteria analysis with specific focuses on bacteria detection, bacteria identification and discrimination, as well as bacteria antibiotic susceptibility testing in 2022. The development of novel substrates, the combination with other techniques, and the utilization of advanced data processing tools for the improvement of Raman spectroscopy and future directions are discussed.

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Section: Sers Substrate Modificationmentioning

confidence: 98%

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

Recent advances of Raman spectroscopy for the analysis of bacteria

Rodriguez

Zhang

Wang

2023

Analytical Science Advances

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“…Specifically, the combination of SERS and machine learning algorithms has shown considerable potential in many fields. [ 12–16 ]…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the combination of SERS and machine learning algorithms has shown considerable potential in many fields. [12][13][14][15][16] Here, we adopted a strategy that has been developed in SERS artificial nose analysis to identify complex samples, taking green tea with different storage times as an example. It is well known that green tea is an extremely complex flavour product, containing large amounts of compounds such as polyphenols, caffeine, amino acids, and vitamins.…”

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confidence: 99%

Surface‐enhanced Raman scattering integrating with machine learning for green tea storage time identification

et al. 2023

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The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface‐enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface‐functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)‐based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS‐based PCA‐SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

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“…[12,13] Primarily due to its nondestructive characteristics, the application scope of SERS has been gradually expanding. [14][15][16][17] However, due to the hydrophobicity of PAHs, only a weak affinity was found for these chemicals when adsorbing onto metallic surfaces, resulting in a poor SERS response, which greatly limited the application of SERS in PAH detection. [18] To date, various modification methods have been applied to change the surface properties of the substrate.…”

Section: Introductionmentioning

confidence: 99%

Halogen ion‐modified silver nanoparticles for ultrasensitive surface‐enhanced Raman spectroscopy detection of polycyclic aromatic hydrocarbons

et al. 2022

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Rapid ultrasensitive detection of trace polycyclic aromatic hydrocarbons (PAHs) is essential and significant for pollution control due to their hazard, persistence, and the wide distribution in the environment. Therefore, rapid detection of PAHs is critical for controlling pollution and protecting the ecology. Considering the advantages of surface-enhanced Raman spectroscopy (SERS), a simple and reliable SERS method was proposed in this work for detecting PAHs in water. Three chemicals, namely NaCl, KBr, and KI, were chosen to modify Ag nanoparticles (NPs) for phenanthrene (Phe) detection, and Ag NPs modified with KBr (Ag-Br NPs) showed the best SERS response. The mixing sequence and the concentration of KBr were optimized. The addition order of mixing KBr and Ag NPs before Phe solution was the best sequence, and the optimal concentration of KBr was 20 mM. Under optimal conditions, the limits of quantification for Phe, pyrene (Pyr), and anthracene (Ant) were 10 À6 M, 10 À7 M, and 10 À7 M, respectively. Mixed PAHs (Phe, Pyr, and Ant) in spiked water samples were identified and quantified successfully. The proposed method has good application prospects in environmental pollution monitoring.

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Boosting bacteria differentiation efficiency with multidimensional surface‐enhanced Raman scattering: the example of Bacillus cereus

Cited by 8 publications

References 57 publications

Recent advances of Raman spectroscopy for the analysis of bacteria

Recent advances of Raman spectroscopy for the analysis of bacteria

Surface‐enhanced Raman scattering integrating with machine learning for green tea storage time identification

Halogen ion‐modified silver nanoparticles for ultrasensitive surface‐enhanced Raman spectroscopy detection of polycyclic aromatic hydrocarbons

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