Label-free strip sensor based on surface positively charged nitrogen-rich carbon nanoparticles for rapid detection of Salmonella enteritidis
Immunoassay
Models, Molecular
Nitrogen
Biosensing Techniques
01 natural sciences
Carbon
0104 chemical sciences
3. Good health
Salmonella enteritidis
Limit of Detection
Salmonella Infections
Food Microbiology
Humans
Nanoparticles
Antibodies, Immobilized
Food Analysis
Reagent Strips
DOI:
10.1016/j.bios.2019.02.061
Publication Date:
2019-03-05T12:44:32Z
AUTHORS (9)
ABSTRACT
Lateral flow immunoassay (LFIA) is a class and widespread applied point-of-care biosensor in the rapid monitoring field. To address the matched antibodies and antibody labeling dependence in the conventional LFIAs, in this work, an innovative label-free LFIA was proposed for the sensitive detection of Salmonella enteritidis (S. enteritidis) by introducing a new nanoparticles-bacteria-antibody sandwich strategy in the sensor. Surface positively charged nitrogen-rich carbon (pNC) nanoparticles, synthesized via calcination and etching reactions, were used as adsorbent to capture bacteria as well as for generating signals. In the presence of target pathogens, bacterial cells could combine with pNC through electrostatic interaction and hydrogen bonding, then the complex would be captured specifically by the anti-bacteria monoclonal antibody (McAb) coated on the test line (T-line). With the accumulation of nanoparticles-bacteria, the color on T-line would be gradually deepened from nearly colorless to deep black. Importantly, the pNC-based immunoassay could exhibit high sensitivity for target pathogens detection with a linear range of 102-108 cfu mL-1 and a low detection limit of 102 cfu mL-1. Furthermore, this system was validated preliminarily to screen S. enteritidis in different food samples with recoveries ranging from 85% to 110%. Taking advantages of simplicity, label-free, convenience, and sensitivity, the pNC-based LFIA has the application potential for pathogenic microorganisms monitoring in food safety and early clinical diagnosis fields.
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