Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra

Tang, Jia-Wei; Li, Jiaqi; Yin, Xiao-Cong; Xu, Wenwen; Pan, Ya-Cheng; Liu, Qinghua; Gu, Bing; Zhang, Xiao; Wang, Liang

doi:10.3389/fmicb.2022.843417

Cited by 25 publications

(12 citation statements)

References 22 publications

(29 reference statements)

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“…Due to the complexity of SERS spectral data, classical statistical methods are insufficient to analyse the Raman spectral data 26 . Therefore, machine learning algorithms have been recruited to analyse and predict the identification of the SERS spectrum.…”

Section: Methodsmentioning

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

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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.

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Section: Methodsmentioning

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

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“…68 There are also other models frequently used in the Raman spectra analysis such as least squares SVM (LS-SVM) 74 and K-means clustering. 68 LS-SVM is an extension of standard SVM and uses a least square linear system as the loss function.…”

Section: Advanced Data Processing Methodsmentioning

confidence: 99%

“…Both models were found to have similarly high sensitivity, specificity, and accuracy. Another study compared multiple machine learning algorithms to discriminate clinically important pathogens, and CNN achieved the highest prediction accuracy 68 . There are also other models frequently used in the Raman spectra analysis such as least squares SVM (LS‐SVM) 74 and K‐means clustering 68 .…”

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

“…Another study compared multiple machine learning algorithms to discriminate clinically important pathogens, and CNN achieved the highest prediction accuracy 68 . There are also other models frequently used in the Raman spectra analysis such as least squares SVM (LS‐SVM) 74 and K‐means clustering 68 . LS‐SVM is an extension of standard SVM and uses a least square linear system as the loss function.…”

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

“…The principle of bacteria identification and discrimination by Raman spectroscopy with machine learning for data processing is shown in Figure3C. In particular, convolutional neural networks (CNN)[68][69][70] and support vector machine (SVM)67,71,72 have received widespread attention in Raman spectra analysis. SVM is an optimization-based machine learning technique which increases the data dimensionality to convert a nonlinear classification into a linear one.…”

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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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Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Al‐Shaebi,

Akdeniz,

Ahmed

et al. 2023

Adv Materials Inter

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Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are imperative. Surface‐enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide.

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Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra

Cited by 25 publications

References 22 publications

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

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

Recent advances of Raman spectroscopy for the analysis of bacteria

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

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