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Rapid Identification of Pathogens

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Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural

Jingyu Ding¹, Qingqing Lin², Jiameng Zhang²

¹College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China.

Analytical and Bioanalytical Chemistry

|May 7, 2021

Summary

A new method combining surface-enhanced Raman spectroscopy (SERS) and multi-scale convolutional neural networks (CNN) accurately identifies Salmonella serovars. This approach achieved over 97% accuracy, offering a promising tool for pathogen detection.

Keywords:

Identification Multi-scale convolutional neural network Salmonella serovars Surface-enhanced Raman scattering (SERS)

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Area of Science:

Analytical Chemistry
Microbiology
Machine Learning

Background:

Salmonella is a major global pathogen responsible for significant illness and death.
Over 2600 Salmonella serovars exist, with Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi being common foodborne pathogens.
Accurate and efficient detection methods are crucial for public health.

Purpose of the Study:

To develop an efficient analytical method for detecting and distinguishing common Salmonella serovars.
To combine surface-enhanced Raman spectroscopy (SERS) with a multi-scale convolutional neural network (CNN) for Salmonella serotype identification.

Main Methods:

Preparation of 34-nm gold nanoparticles (AuNPs) as label-free Raman substrates.
Measurement of 1854 SERS spectra from Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi.
Development of a multi-scale CNN model for multi-dimensional SERS spectral feature extraction.

Main Results:

The multi-scale CNN model achieved a recognition accuracy exceeding 97%.
The study analyzed the impact of training iterations and sample size on recognition accuracy.
Experimental data validated the model's high performance.

Conclusions:

The combined SERS and multi-scale CNN approach is a feasible and effective method for Salmonella serotype identification.
This technique shows potential for identifying other bacterial species and serovars.
The developed method offers a significant advancement in rapid pathogen detection.