No abstract

Food and waterborne diseases pose considerable public health threats even in highly industrialized parts of the world. Examples of these pathogens in food can be Escherichia coli O157: H7, Salmonella sp., and Listeria monocytogenes. Rapid, reliable detection of pathogens mitigates serious health problems and economic losses due to outbreaks and robust tests safeguard the food supply. In this study, a smartphone-based apparatus was employed to demonstrate quantitative detection of E. coli. To validate the applicability of the present smartphone-based fluorescence device, RNA was extracted from the E. coli K-12 strain and amplified using two different primers (dnaK and rpoA) via quantitative polymerase chain reaction (qPCR). Serial dilutions of RNA from 10 to 0.0001 ng/µL were prepared at the start of the PCR amplification and the PCR products were detected by CYBR Green1-based fluorescence. For a proof-of-concept test for the smartphone system, samples from these PCR products were then analyzed. The detection system employed a novel algorithm to analyze fluorescence signals and read changes in E. coli DNA concentration. The correlations between the fluorescence percentage and DNA concentrations were R=0.945 for the dnaK primer and R=0.893 for the rpoA primer, respectively. Utilizing this new fluorescent analysis technique resulted in comparable accuracy to the real-time PCR fluorescent signal detection. The key innovation of this approach was to combine efficient image processing encoded into a smartphone application with a low-cost 3-D printed device that allowed quantification of bacterial nucleic acid.