Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

Sun, Jiazheng; Xu, Xuefang; Feng, Songsong; Zhang, Hanyu; Xu, Lingfeng; Jiang, Hong; Sun, Baibing; Meng, Yuyan; Chen, Weizhou

doi:10.1016/j.talanta.2022.123807

Cited by 28 publications

(18 citation statements)

References 39 publications

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“…126,127 Nevertheless, infrared spectroscopy and Raman spectroscopy are considered to be complementary techniques. 128–130…”

Section: Quantitative Methods For Monitoring Photopolymerization Reac...mentioning

confidence: 99%

“…118 126,127 Nevertheless, infrared spectroscopy and Raman spectroscopy are considered to be complementary techniques. [128][129][130] The polarizability of the electrons in a molecule corresponds with the intensity of the Raman effect. The molecule is briefly in a virtual energy state as a result of this excitation before the photon is released.…”

Section: Polymer Chemistry Reviewmentioning

confidence: 99%

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Review of quantitative and qualitative methods for monitoring photopolymerization reactions

et al. 2023

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“…126,127 Nevertheless, infrared spectroscopy and Raman spectroscopy are considered to be complementary techniques. 128–130…”

Section: Quantitative Methods For Monitoring Photopolymerization Reac...mentioning

confidence: 99%

Section: Polymer Chemistry Reviewmentioning

confidence: 99%

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

et al. 2023

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“…The AI-based classifier was able to achieve its highest accuracy of 92.9% for the center ROI dataset, which can also instantly predict spectra, hence efficiently and immediately predicting and identifying live foodborne pathogens at the single-cell level. Similarly, Raman spectroscopy and machine learning algorithm was used by Sun et al (2023) to identify Salmonella spp. rapidly.…”

Section: Emerging Biosensing Technologies and Approachesmentioning

confidence: 99%

Advances, applications, and limitations of portable and rapid detection technologies for routinely encountered foodborne pathogens

et al. 2022

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Traditional foodborne pathogen detection methods are highly dependent on pre-treatment of samples and selective microbiological plating to reliably screen target microorganisms. Inherent limitations of conventional methods include longer turnaround time and high costs, use of bulky equipment, and the need for trained staff in centralized laboratory settings. Researchers have developed stable, reliable, sensitive, and selective, rapid foodborne pathogens detection assays to work around these limitations. Recent advances in rapid diagnostic technologies have shifted to on-site testing, which offers flexibility and ease-of-use, a significant improvement from traditional methods’ rigid and cumbersome steps. This comprehensive review aims to thoroughly discuss the recent advances, applications, and limitations of portable and rapid biosensors for routinely encountered foodborne pathogens. It discusses the major differences between biosensing systems based on the molecular interactions of target analytes and biorecognition agents. Though detection limits and costs still need further improvement, reviewed technologies have high potential to assist the food industry in the on-site detection of biological hazards such as foodborne pathogens and toxins to maintain safe and healthy foods. Finally, this review offers targeted recommendations for future development and commercialization of diagnostic technologies specifically for emerging and re-emerging foodborne pathogens.

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“…These oscillators interact with the light and are detected as Raman scattering [ 14 , 16 ]. Especially, the shorter wavelength excitation is useful for resonance effects of the Raman scattering and selectivity of target molecules [ 14 , 17 ]. One disadvantage of the resonance effects is excessive excitation that may cause disturbance of visible light-induced physiological phenomena and/or damages in viability of cells [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

Cited by 28 publications

References 39 publications

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

Review of quantitative and qualitative methods for monitoring photopolymerization reactions

Advances, applications, and limitations of portable and rapid detection technologies for routinely encountered foodborne pathogens

Raman spectral analysis of microbial pigment compositions in vegetative cells and heterocysts of multicellular cyanobacterium

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