Rapid identification of live and dead Salmonella by surface-enhanced Raman spectroscopy combined with convolutional neural network

Zhang, Jianhua; Zhang, Jiameng; Ding, Jingyu; Lin, Qingqing; Young, Glenn M.

doi:10.1016/j.vibspec.2021.103332

Cited by 14 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The principle of bacteria identification and discrimination by Raman spectroscopy with machine learning for data processing is shown in Figure 3C . 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.…”

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

Recent advances of Raman spectroscopy for the analysis of bacteria

Rodriguez

Zhang

Wang

2023

Analytical Science Advances

View full text Add to dashboard Cite

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.

show abstract

Section: Bacteria Identification and Discriminationmentioning

confidence: 99%

Recent advances of Raman spectroscopy for the analysis of bacteria

Rodriguez

Zhang

Wang

2023

Analytical Science Advances

View full text Add to dashboard Cite

show abstract

“…Data imbalance is a well-known scenario in medical imaging when training using a deep neural network ( Johnson & Khoshgoftaar, 2019 ; Cano et al, 2021 ). Zhang et al (2022) used a balanced total data sample for training and testing to identify three bacteria classes to overcome this problem. A total of 3,059 images of Salmonella Enteritidis, 3,110 images of Salmonella Paratyphoid, and 3,092 images of Salmonella Typhimurium were used in this work.…”

Section: Evolution Of Cell Analysis Approachesmentioning

confidence: 99%

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Zamani

Zaki

Hamid

et al. 2022

PeerJ

View full text Add to dashboard Cite

Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning.

show abstract

“…15 Therefore, the SERS technique has potential in biomolecular detection due to its high resolution, high sensitivity, low solution interference and strong robustness. 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.…”

Section: Introductionmentioning

confidence: 99%

“…Later, the development of surface‐enhanced Raman spectroscopy (SERS) greatly improved the magnitude of normal Raman signals 15 . Therefore, the SERS technique has potential in biomolecular detection due to its high resolution, high sensitivity, low solution interference and strong robustness 16 . Recent studies have increasingly employed SERS technology to detect microbial pathogens 17,18 .…”

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

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

Rapid identification of live and dead Salmonella by surface-enhanced Raman spectroscopy combined with convolutional neural network

Cited by 14 publications

References 21 publications

Recent advances of Raman spectroscopy for the analysis of bacteria

Recent advances of Raman spectroscopy for the analysis of bacteria

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

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

Contact Info

Product

Resources

About