scholarly journals Detection and Characterization of Antibiotic-Resistant Bacteria Using Surface-Enhanced Raman Spectroscopy

Nanomaterials ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 762 ◽  
Author(s):  
Kaidi Wang ◽  
Shenmiao Li ◽  
Marlen Petersen ◽  
Shuo Wang ◽  
Xiaonan Lu
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Clinical Samples ◽  
Resistant Bacteria ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Whole Cells ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

This mini-review summarizes the most recent progress concerning the use of surface-enhanced Raman spectroscopy (SERS) for the detection and characterization of antibiotic-resistant bacteria. We first discussed the design and synthesis of various types of nanomaterials that can be used as the SERS-active substrates for biosensing trace levels of antibiotic-resistant bacteria. We then reviewed the tandem-SERS strategy of integrating a separation element/platform with SERS sensing to achieve the detection of antibiotic-resistant bacteria in the environmental, agri-food, and clinical samples. Finally, we demonstrated the application of using SERS to investigate bacterial antibiotic resistance and susceptibility as well as the working mechanism of antibiotics based on spectral fingerprinting of the whole cells.

scholarly journals Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatma Uysal Ciloglu ◽  
Abdullah Caliskan ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Resistant Bacteria ◽  
High Signal ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractOver the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

scholarly journals Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

2021 ◽  
Author(s):  
Fatma Uysal Ciloglu ◽  
Abdullah Caliskan ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Resistant Bacteria ◽  
Label Free ◽  
Antibiotic Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Abstract Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door - antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with other traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared with other traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data. The proposed method is a label-free, easy implemented, and reliable technique with high sensitivity for clinical use.

scholarly journals Refractive index sensing and surface-enhanced Raman spectroscopy using silver–gold layered bimetallic plasmonic crystals

2017 ◽  
Vol 8 ◽  
pp. 2492-2503 ◽  
Author(s):  
Somi Kang ◽  
Sean E Lehman ◽  
Matthew V Schulmerich ◽  
An-Phong Le ◽  
Tae-woo Lee ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Refractive Index ◽  
Electric Fields ◽  
Surface Enhanced Raman Spectroscopy ◽  
Plasmonic Crystals ◽  
Metal Thickness ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Difference Time

Herein we describe the fabrication and characterization of Ag and Au bimetallic plasmonic crystals as a system that exhibits improved capabilities for quantitative, bulk refractive index (RI) sensing and surface-enhanced Raman spectroscopy (SERS) as compared to monometallic plasmonic crystals of similar form. The sensing optics, which are bimetallic plasmonic crystals consisting of sequential nanoscale layers of Ag coated by Au, are chemically stable and useful for quantitative, multispectral, refractive index and spectroscopic chemical sensing. Compared to previously reported homometallic devices, the results presented herein illustrate improvements in performance that stem from the distinctive plasmonic features and strong localized electric fields produced by the Ag and Au layers, which are optimized in terms of metal thickness and geometric features. Finite-difference time-domain (FDTD) simulations theoretically verify the nature of the multimode plasmonic resonances generated by the devices and allow for a better understanding of the enhancements in multispectral refractive index and SERS-based sensing. Taken together, these results demonstrate a robust and potentially useful new platform for chemical/spectroscopic sensing.

Identification and Characterization of Artists' Red Dyes and Their Mixtures by Surface-Enhanced Raman Spectroscopy

2007 ◽  
Vol 61 (9) ◽  
pp. 994-1000 ◽  
Author(s):  
Alyson V. Whitney ◽  
Francesca Casadio ◽  
Richard P. Van Duyne
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Localized Surface Plasmon ◽  
Carminic Acid ◽  
Sers Substrates ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Lac Dye ◽  
Silver Island

Silver film over nanospheres (AgFONs) were successfully employed as surface-enhanced Raman spectroscopy (SERS) substrates to characterize several artists' red dyes including: alizarin, purpurin, carminic acid, cochineal, and lac dye. Spectra were collected on sample volumes (1 × 10−6 M or 15 ng/μL) similar to those that would be found in a museum setting and were found to be higher in resolution and consistency than those collected on silver island films (AgIFs). In fact, to the best of the authors' knowledge, this work presents the highest resolution spectrum of the artists' material cochineal to date. In order to determine an optimized SERS system for dye identification, experiments were conducted in which laser excitation wavelengths were matched with correlating AgFON localized surface plasmon resonance (LSPR) maxima. Enhancements of approximately two orders of magnitude were seen when resonance SERS conditions were met in comparison to non-resonance SERS conditions. Finally, because most samples collected in a museum contain multiple dyestuffs, AgFONs were employed to simultaneously identify individual dyes within several dye mixtures. These results indicate that AgFONs have great potential to be used to identify not only real artwork samples containing a single dye but also samples containing dyes mixtures.

scholarly journals Surface-Enhanced Raman Spectroscopy Characterization of Breast Cell Phenotypes: Effect of Nanoparticle Geometry

2019 ◽  
Vol 2 (11) ◽  
pp. 6960-6970
Author(s):  
Richard E. Darienzo ◽  
Jingming Wang ◽  
Olivia Chen ◽  
Maurinne Sullivan ◽  
Tatsiana Mironava ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Cell Phenotypes

Applications of surface-enhanced Raman spectroscopy in detection fields

Nanomedicine ◽  
2020 ◽  
Vol 15 (30) ◽  
pp. 2971-2989 ◽  
Author(s):  
Ting Lin ◽  
Ya-Li Song ◽  
Juan Liao ◽  
Fang Liu ◽  
Ting-Ting Zeng
Keyword(s):  
Raman Spectroscopy ◽  
Medical Diagnosis ◽  
High Selectivity ◽  
Surface Enhanced Raman Spectroscopy ◽  
Molecular Structures ◽  
Spectroscopy Technique ◽  
Detection Mechanism ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) is a Raman spectroscopy technique that has been widely used in food safety, environmental monitoring, medical diagnosis and treatment and drug monitoring because of its high selectivity, sensitivity, rapidness, simplicity and specificity in identifying molecular structures. This review introduces the detection mechanism of SERS and summarizes the most recent progress concerning the use of SERS for the detection and characterization of molecules, providing references for the later research of SERS in detection fields.

Sign in / Sign up

Export Citation Format

Share Document