Characterization and Optimization of the Smartphone Response to Paper Microfluidic Biosensor Assay Under UV Light Source

Abstract

The use of smartphone for the detection of biological constituents is becoming a useful tool as a point-of-care (POC) device and diagnostics. When combined with microfluidic paper analytic devices (μPAD) and particle immunoassays, we have the ability to detect bacterial pathogens with sensitivity and specificity. Environmental conditions as well as variability in smartphone imaging and the cellulose in paper microfluidics however can sometimes easily interfere with the detection of small signal changes. Combining this issue with the detection of pathogens in blood (our model biological sample of interest) becomes difficult with such a platform because of the complexity of the sample matrix. However, in this research we take a novel approach at utilizing polystyrene’s auto-fluorescence and the high energy of UV LEDs in a particle immunoassay in order to increase our signal change. We first characterized how the smartphone actually responds to UV light (275-385 nm) with respect to the RGB components in its images. We were then able to determine a favorable response using the 385 nm UV LED. The detection of green fluorescence by polystyrene particles was possible by analyzing the smartphone’s image in the green channel. There was a significant difference in signal change with blood samples including polystyrene versus just blood samples with a normalized signal intensity change of 2.5 (150%). The detection of polystyrene fluorescence was translated into a field deployable prototype, where preliminary trials showed promising results in detecting Escherichia coli in blood with a current limit of detection of 50 CFU/ml. With further experimentation and optimization the limit of detection could be improved to 10 CFU/mL, making it a very useful tool in the detection of blood borne pathogens to prevent complications with onset bacteremia and the more serious cases of sepsis. This assay platform could provide an easy to use solution with detection in a short time (assay time of 1 min) compared to the lengthy blood culture monitoring or biomarker detection.