|Kim, Huisung - Purdue University|
|Jung, Youngkee - Purdue University|
|Doh, Iyii-joon - Purdue University|
|Lozano-mahecha, Roxana - Purdue University|
|Applegate, Bruce - Purdue University|
|Bae, Euiwon - Purdue University|
Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/5/2016
Publication Date: 1/9/2017
Citation: Kim, H., Jung, Y., Doh, I., Lozano-Mahecha, R.A., Applegate, B., Bae, E. 2017. Smartphone-based low light detection for bioluminescence application. Scientific Reports. doi: 10.1038/srep40203.
Interpretive Summary: Light-producing bioluminescent technologies have been used to develop very sensitive methods for detection of contaminants related to food safety and environmental monitoring. Due to the extremely low level of light intensity, specialized benchtop equipment is typically used for these bioluminescent assays. Hardware and associated software were developed to transform smartphones into ultra-low light detection devices that can be used in bioluminescence detection. Using a model test bacterium, a commercial smartphone was able reproducibly detect the presence of the bacterium. While increases in assay sensitivity will be necessary, the application of the hardware design and software algorithm can transform typical smartphones into portable analytical instruments. These smartphone instruments can be used by farmers, inspectors, and researchers to rapidly detect the presence of contaminants in food or environmental samples on-site, without shipping them to a central laboratory for testing.
Technical Abstract: We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an analyte. A simple cradle that houses the smartphone, sample tube, and collection lens supports the measuring platform, while noise reduction by ensemble averaging simultaneously lowers the background and enhances the signal from emitted photons. Five different types of smartphones, both Android and iOS devices, were tested, and the top two candidates were used to evaluate luminescence from the bioluminescent reporter Pseudomonas fluorescens M3A. The best results were achieved by OnePlus One (android), which was able to detect luminescence from approximately 10X6 CFU/mL of the bio-reporter, which corresponds to approximately 10X7 photons/s with 180 seconds of integration time.