Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

The Analyst ◽  
2020 ◽  
Vol 145 (23) ◽  
pp. 7559-7570
Author(s):  
Fatma Uysal Ciloglu ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
Mehmet Kahraman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Techniques ◽  
Methicillin Resistant ◽  
Learning Techniques ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Herein, surface-enhanced Raman spectroscopy (SERS) combined with supervised and unsupervised machine learning techniques were used for the identification of methicillin-resistant and methicillin-sensitive Staphylococcus aureus.

Rapid and specific duplex detection of methicillin-resistant Staphylococcus aureus genes by surface-enhanced Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (7) ◽  
pp. 2789-2794 ◽  
Author(s):  
Phani R. Potluri ◽  
Vinoth Kumar Rajendran ◽  
Anwar Sunna ◽  
Yuling Wang
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Early Diagnosis ◽  
Rapid Detection ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Surface Enhanced Raman Spectroscopy ◽  
Specific Method ◽  
Accurate Identification ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

A highly specific method for rapid detection of MRSA genes has been proposed by combining surface-enhanced Raman spectroscopy nanotags and magnetic isolation, which shows great potential for accurate identification of MRSA at an early-diagnosis stage.

scholarly journals Novel Surface-Enhanced Raman Spectroscopy Techniques for DNA, Protein and Drug Detection

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1712 ◽  
Author(s):  
Chuanpin Chen ◽  
Wenfang Liu ◽  
Sanping Tian ◽  
Tingting Hong
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Spectroscopic Technique ◽  
Machine Learning Techniques ◽  
Drug Detection ◽  
Learning Techniques ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Sers Detection ◽  
Scattering Signal

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique in which the Raman scattering signal strength of molecules, absorbed by rough metals or the surface of nanoparticles, experiences an exponential growth (103–106 times and even 1014–1015 times) because of electromagnetic or chemical enhancements. Nowadays, SERS has attracted tremendous attention in the field of analytical chemistry due to its specific advantages, including high selectivity, rich informative spectral properties, nondestructive testing, and the prominent multiplexing capabilities of Raman spectroscopy. In this review, we present the applications of state-of-the-art SERS for the detection of DNA, proteins and drugs. Moreover, we focus on highlighting the merits and mechanisms of achieving enhanced SERS signals for food safety and clinical treatment. The machine learning techniques, combined with SERS detection, are also indicated herein. This review concludes with recommendations for future studies on the development of SERS.

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (14) ◽  
pp. 4827-4835 ◽  
Author(s):  
Shizhuang Weng ◽  
Hecai Yuan ◽  
Xueyan Zhang ◽  
Pan Li ◽  
Ling Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Learning Networks ◽  
Biological Detection ◽  
Machine Learning Methods ◽  
Intelligent Analysis ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.

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 Specific detection of Staphylococcus aureus infection and marker for Alzheimer disease by surface enhanced Raman spectroscopy using silver and gold nanoparticle-coated magnetic polystyrene beads

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Prucek ◽  
Aleš Panáček ◽  
Žaneta Gajdová ◽  
Renata Večeřová ◽  
Libor Kvítek ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Alzheimer Disease ◽  
Surface Enhanced Raman Spectroscopy ◽  
Sers Substrate ◽  
Magnetic Composites ◽  
Protein Marker ◽  
Promising Alternative ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractTargeted and effective therapy of diseases demands utilization of rapid methods of identification of the given markers. Surface enhanced Raman spectroscopy (SERS) in conjunction with streptavidin–biotin complex is a promising alternative to culture or PCR based methods used for such purposes. Many biotinylated antibodies are available on the market and so this system offers a powerful tool for many analytical applications. Here, we present a very fast and easy-to-use procedure for preparation of streptavidin coated magnetic polystyrene–Au (or Ag) nanocomposite particles as efficient substrate for surface SERS purposes. As a precursor for the preparation of SERS active and magnetically separable composite, commercially available streptavidin coated polystyrene (PS) microparticles with a magnetic core were utilized. These composites of PS particles with silver or gold nanoparticles were prepared by reducing Au(III) or Ag(I) ions using ascorbic acid or dopamine. The choice of the reducing agent influences the morphology and the size of the prepared Ag or Au particles (15–100 nm). The prepare composites were also characterized by HR-TEM images, mapping of elements and also magnetization measurements. The content of Au and Ag was determined by AAS analysis. The synthesized composites have a significantly lower density against magnetic composites based on iron oxides, which considerably decreases the tendency to sedimentation. The polystyrene shell on a magnetic iron oxide core also pronouncedly reduces the inclination to particle aggregation. Moreover, the preparation and purification of this SERS substrate takes only a few minutes. The PS composite with thorny Au particles with the size of approximately 100 nm prepared was utilized for specific and selective detection of Staphylococcus aureus infection in joint knee fluid (PJI) and tau protein (marker for Alzheimer disease).

scholarly journals Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia-Wei Tang ◽  
Qing-Hua Liu ◽  
Xiao-Cong Yin ◽  
Ya-Cheng Pan ◽  
Peng-Bo Wen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

scholarly journals SERS multiplexing of methylxanthine drug isomers via host-guest size matching and machine learning

2021 ◽  
Author(s):  
Weng-I Katherine Chio ◽  
Jia Liu ◽  
Tabitha Jones ◽  
Jayakumar Perumal ◽  
U. S. Dinish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Multiplexed Detection ◽  
Drug Molecules ◽  
Similar Drug ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Detection And Quantification

Multiplexed detection and quantification of structurally similar drug molecules, methylxanthine MeX, incl. theobromine TBR, theophylline TPH and caffeine CAF, have been demonstrated via solution-based surface-enhanced Raman spectroscopy (SERS), achieving highly...

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.

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