One-Step Detection of Salmonella by a Nanorobots Combined Endonuclease Signal Amplification with Inductively Coupled Plasma Mass Spectrometry

36 Pages Posted: 13 Nov 2024

See all articles by Xiaoting Li

Xiaoting Li

affiliation not provided to SSRN

Xin Li

affiliation not provided to SSRN

Kaixi Hong

affiliation not provided to SSRN

Dong Liu

affiliation not provided to SSRN

Bin Li

affiliation not provided to SSRN

Li Wang

South China Normal University

Abstract

Foodborne pathogens pose a great threat to human health. Sensitive and rapid detection of foodborne pathogens is crucial for preventing the food-borne diseases. In this work, a nanorobot was designed to combine endonuclease signal amplification technology with ICP-MS for the sensitive and one-step detection of Salmonella. When the aptamer on the nanorobots specifically recognized Salmonella, the endonuclease signal amplification was initiated to release Au NPs for ICP-MS detection. The limit of detection of 15 CFU mL−1 and the linear ranges of 50-40,000 CFU mL−1 were obtained by this proposed method under optimal conditions. This work only needs one step of magnetic separation to realize the sensitive and rapid detection of Salmonella, avoiding the complex procedures and contamination risks. And it can be applied to various types of sample matrix, including chicken, tomatoes, eggs and milk, demonstrating the great potential in practical application.

Keywords: ICP-MS, endonuclease signal amplification, nanorobots, foodborne pathogens

Suggested Citation

Li, Xiaoting and Li, Xin and Hong, Kaixi and Liu, Dong and Li, Bin and Wang, Li, One-Step Detection of Salmonella by a Nanorobots Combined Endonuclease Signal Amplification with Inductively Coupled Plasma Mass Spectrometry. Available at SSRN: https://ssrn.com/abstract=5019311 or http://dx.doi.org/10.2139/ssrn.5019311

Xiaoting Li (Contact Author)

affiliation not provided to SSRN ( email )

Xin Li

affiliation not provided to SSRN ( email )

Kaixi Hong

affiliation not provided to SSRN ( email )

Dong Liu

affiliation not provided to SSRN ( email )

Bin Li

affiliation not provided to SSRN ( email )

Li Wang

South China Normal University ( email )

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Tianhe District
Guangzhou, 510631, 510642
China

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