Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

Oliveira, Fernanda SantAna de Siqueira e; Silva, Adriano Moraes da; Pacheco, Marcos Tadeu Tavares; Giana, Héctor Enrique; Silveira, Landulfo

doi:10.1007/s10103-020-03028-9

Cited by 35 publications

(25 citation statements)

References 56 publications

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“…According to previous studies, characteristic peaks in Raman spectra corresponded to different biochemical molecules ( 22 ). In addition, the more complex the chemical composition of a bacterium, the richer its Raman spectrum ( 23 ).…”

Section: Resultsmentioning

confidence: 83%

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

et al. 2022

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With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

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Section: Resultsmentioning

confidence: 83%

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

et al. 2022

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“…During the computational analysis of Raman spectra, the whole spectra rather than specific peaks were analyzed, which would also reduce the influences of small variations in the fingerprinting spectra caused by the toxicity of silver nanoparticles. Characteristic peaks could not only reflect the unique patterns of Raman spectra but also correspond to specific compositions of bacterial species ( de Siqueira E Oliveira et al, 2020 ). Thus, it was biologically meaningful to analyze the characteristic peaks of Raman spectrum for each Staphylococcus species.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…In addition, RS has also been applied in single bacterial analysis and live bacterial studies, which could not only minimize the bacterial metabolic variability at the different phases but also facilitate the understanding of cellular dynamics ( Strola et al, 2014 ; Smith et al, 2016 ). In terms of the differentiation of Gram-positive bacteria from Gram-negative bacteria, it was showed that some peaks at 540 and 1,380 cm –1 had significant differences for Gram-positive bacteria when compared with the Gram-negative bacteria, which was mainly attributed to the glycosidic bonds in N -acetyl glucosamine and N -acetyl muramic acid of peptidoglycan ( de Siqueira E Oliveira et al, 2020 ).…”

Section: Bacterial Identificationmentioning

confidence: 99%

“…So far, studies performing RS on clinical bacterial pathogens require culture in agar plates because of the low concentration of bacteria in clinical samples ( Rebrošová et al, 2017 ; de Siqueira E Oliveira et al, 2020 ). Although culture-based RS could provide sufficient biomass during testing, hence higher signal–noise ratio, it is rather time consuming.…”

Section: Bacterial Identificationmentioning

confidence: 99%

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

et al. 2021

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Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

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Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

Cited by 35 publications

References 56 publications

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

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

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

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