Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms

Wang, Liang; Tang, Jia-Wei; Li, Fen; Usman, Muhammad; Wu, Chang-Yu; Liu, Qinghua; Kang, Haiquan; Liu, Wei; Gu, Bing

doi:10.1128/spectrum.02580-22

Cited by 34 publications

(27 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…coli . The result is consistent with previous studies, in which CNN model has been found to be the best for analyzing and predicting bacterial species ( Tang et al, 2021 ; Liu et al, 2022 ; Tang et al, 2022 ; Wang et al, 2022a,b ), though other machine learning algorithms such as SVM and RF also achieved very high levels of accuracy for species identification. Therefore, through this study, it was suggested that the label-free SERS technique coupled with machine learning algorithms could be used for the rapid discrimination of Shigella spp., and Escherichia coli .…”

Section: Discussionsupporting

confidence: 91%

“…As an easy-to-learn, low-cost, non-invasive and label-free method, Raman spectroscopy, especially the surface enhanced Raman spectroscopy due to the significantly enhanced Raman signals, has great application potential for rapid and accurate bacterial pathogen identification in clinical settings, though huge challenges exist and the method has been officially used in real-world situation yet ( Wang et al, 2021 ). Previously, the promising SERS technique has been widely used for both genus/species discrimination and antibiotic resistance profiling in clinically important bacterial pathogens with rapid speed and high accuracy ( Ho et al, 2019 ; Tang et al, 2021 ; Liu et al, 2022 ; Tang et al, 2022 ; Wang et al, 2022a , b ). However, there is currently no reported of using label-free SERS technique coupled with machine learning algorithms for the differentiation of Shigella spp., and E .…”

Section: Discussionmentioning

confidence: 99%

“…The preparation procedures for silver nanoparticles (AgNPs) have been well documents in previous studies ( Tang et al, 2021 ; Liu et al, 2022 ; Tang et al, 2022 ; Wang et al, 2022a,b ). Therefore, we only briefly stated the preparation steps in terms of the key reactions.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Liu

Tang²,

Mou³

et al. 2023

Front. Microbiol.

Self Cite

View full text Add to dashboard Cite

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Liu

Tang²,

Mou³

et al. 2023

Front. Microbiol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…By assessing the abundance of spectral features, we gained insights into each spectrometer's sensitivity and detection capabilities. Additionally, a shaded region representing 20% of the standard deviation (SD) was visualised around the average SERS spectra using Origin Software (OriginLab, United States) 17 . The software's fit peaks (pro) function was employed to automatically fit the spectral characteristic peaks, thereby identifying the corresponding molecular components.…”

Section: Methodsmentioning

confidence: 99%

“…16 Recent studies have increasingly employed SERS technology to detect microbial pathogens. 17,18 Yan et al 19 applied single-cell Raman spectrometry for rapid discrimination of 23 bacterial species across 7 genera. The study first utilized decision tree machine learning algorithms to assess and differentiate single bacterial cells at the serotype level and then constructed a quaternary classification model to elevate the accuracy of various recognition models, thus enabling efficient prediction of bacterial strains at the serotype level.…”

Section: Introductionmentioning

confidence: 99%

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Yuan,

Gu,

Liu

et al. 2024

J Cellular Molecular Medi

Self Cite

View full text Add to dashboard Cite

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface‐enhanced Raman spectroscopy (SERS) is a non‐invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost‐effective identification of closely associated S. enterica serovars.

show abstract

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

Tang,

Wen,

Chen

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

In this study, it is aimed to establish a novel method based on a deep‐learning‐guided surface‐enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short‐term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the SERS fingerprints of four types of vaginal secretions is significantly improved after VAE decoding and reconstruction. The signal‐to‐noise ratio of the generated spectra increased from the original 2.58–11.13. Among all algorithms, the VAE–LSTM algorithm demonstrates the best prediction ability and time efficiency. Additionally, blind test datasets yielded an overall accuracy of 85%. In this study, it is concluded that the deep‐learning‐guided SERS technique holds significant potential in rapidly distinguishing between different levels of vaginal cleanliness through human vaginal secretion samples. This contributes to the efficient diagnosis of vaginal cleanliness levels in clinical settings.

show abstract

Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms

Cited by 34 publications

References 31 publications

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

Contact Info

Product

Resources

About