Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Tewes, Thomas J.; Kerst, Mario; Pavlov, Svyatoslav; Huth, Miriam A.; Hansen, Ute; Bockmühl, Dirk P.

doi:10.1016/j.heliyon.2024.e27824

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2024

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Cited by 2 publications

References 71 publications

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Investigation of the Processing Mechanism of Calcined and Vinegar-Quenched Ochre Samples Using 2t2d Correlation Raman Spectroscopy

Yang,

Li,

et al. 2024

Preprint

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Investigation of the Processing Mechanism of Calcined and Vinegar-Quenched Ochre Samples Using 2t2d Correlation Raman Spectroscopy

Yang,

Li,

et al. 2024

Preprint

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Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications

Rahman,

Sikder,

Tripathi

et al. 2024

Chemosensors

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Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.

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Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

Cited by 2 publications

References 71 publications

Investigation of the Processing Mechanism of Calcined and Vinegar-Quenched Ochre Samples Using 2t2d Correlation Raman Spectroscopy

Investigation of the Processing Mechanism of Calcined and Vinegar-Quenched Ochre Samples Using 2t2d Correlation Raman Spectroscopy

Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications

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