Issue 36, 2017

Fast detection of Escherichia coli in food using nanoprobe and ATP bioluminescence technology

Abstract

In recent years, high demands on food safety technology have made precision and accurate detection technology an urgent research topic. This paper presents a novel biochemical system prototype for colony counting of Escherichia coli using a nano-probe associated with adenosine triphosphate (ATP). The novelty of this system is that the magnetic nanoparticle microbeads are modified with specific antibodies as a probe to capture and enrich the E. coli (Escherichia coli) via the specific connection between antigen and antibody. An experimental test-prototype has been developed in which the light is emitted by an ATP bioluminescence mechanism, the light signal is transformed into an electric signal through a photoelectric multiplier tube, and the quantity of E. coli can be rapidly detected indirectly by a signal processing module. For a testing period of less than twenty minutes, the detectable bacteria concentration range reaches 102–108 CFU mL−1. Under optimal testing conditions, the limit of detection (LOD) is only 3.0 × 102 CFU mL−1. The data measured in independent experiments show that the maximum fluctuation value of the SNR (signal to noise ratio) is below 6.98% which demonstrates that the prototype has excellent reliability and repeatability. The signal processing module consists of a high-precision chopper amplifier with differential magnification up to 3010 times and a SNR less than 50 dB. Furthermore, the linear correlation coefficient of the prototype is 0.964. It can be seen that the system performance is significant.

Graphical abstract: Fast detection of Escherichia coli in food using nanoprobe and ATP bioluminescence technology

Article information

Article type
Paper
Submitted
01 Jul 2017
Accepted
19 Aug 2017
First published
21 Aug 2017

Anal. Methods, 2017,9, 5378-5387

Fast detection of Escherichia coli in food using nanoprobe and ATP bioluminescence technology

Z. Zhang, C. Wang, L. Zhang, Q. Meng, Y. Zhang, F. Sun and Y. Xu, Anal. Methods, 2017, 9, 5378 DOI: 10.1039/C7AY01607G

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