| Title |
Portable nuclear magnetic resonance biosensor and assay for a highly sensitive and rapid detection of foodborne bacteria in complex matrices
|
|---|---|
| Published in |
Journal of Biological Engineering, March 2017
|
| DOI | 10.1186/s13036-017-0053-8 |
| Pubmed ID | |
| Authors |
Yilun Luo, Evangelyn C. Alocilja |
| Abstract |
Nuclear magnetic resonance (NMR) technique is a powerful analytical tool in determining the presence of bacterial contaminants in complex biological samples. In this paper, a portable NMR-based (pNMR) biosensor and assay to detect the foodborne bacteria Escherichia coli O157:H7 is reported. It uses antibody-functionalized polymer-coated magnetic nanoparticles as proximity biomarker of the bacteria which accelerates NMR resonance signal decay. The pNMR biosensor operates at 0.47 Tesla of magnetic strength and consists of a high-power pulsed RF transmitter and an ultra-low noise sensing circuitry capable of detecting weak NMR signal at 0.1 μV. The pNMR biosensor assay and sensing mechanism is used in detecting E. coli O157:H7 bacteria in drinking water and milk samples. Experimental results demonstrate that by adding a filtration step in the assay, the pNMR biosensor is able to detect E. coli O157:H7 as low as 76 CFU/mL in water samples and as low as 92 CFU/mL in milk samples in about one min. The pNMR biosensor assay and sensing system is innovative for foodborne bacterial detection in food matrices. The lowest detection level for E. coli O157:H7 in water and milk samples is essentially 10(1) CFU/mL. Although the linear range of detection is only from 10(1) to 10(4) CFU/mL, the wider detection range spans from 10(1) CFU/mL to 10(7) CFU/mL. Existing pNMR biosensors have detection limits at 10(3)-10(4) CFU/mL only. The detection technique can be extended to other microbial or viral organisms by merely changing the specificity of the antibodies. Besides food safety, the pNMR biosensor described in this paper has potential to be applied as a rapid detection device in biodefense and healthcare diagnostic applications. |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 23% |
| Student > Master | 10 | 14% |
| Researcher | 8 | 11% |
| Student > Bachelor | 7 | 10% |
| Student > Doctoral Student | 5 | 7% |
| Other | 5 | 7% |
| Unknown | 19 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 16 | 23% |
| Chemistry | 7 | 10% |
| Agricultural and Biological Sciences | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 4 | 6% |
| Immunology and Microbiology | 2 | 3% |
| Other | 8 | 11% |
| Unknown | 27 | 39% |