We decided that the best way to prototype our system would be to develop an app that consumers could use to easily read the stickers on fruit. The app also comes to bear on the other side of the equation, workers can use it to read industrial sticker readouts during transport or storage.
The user can identify which specific region of the screen to analyze by tapping the screen to take a photo, which is then broken down into red, green and blue components. The blue intensity is measured, and this is mapped to corresponding ethylene concentrations as informed by our modelling efforts. Unfortunately, our biological system did not progress to full operation, so we were unable to set the scale on real data.
FRES(H) was programmed in the SWIFT language, native for iOS platforms.
Their team aimed to diagnose Chlamydia via the fluorescence of GFP. For this they required an app very similar to our own. A particularly key modification was incorporating the formula for fluorescent intensity into their app, as visible light is not merely a sum of the RGB components.
Instead, it follows this formula - (0.2126*red + 0.7152*green + 0.0722*blue)/5. The app we wrote for them compares the negative, positive and patient micro-fluidic wells visible to the smartphone camera, and uses this to set a threshold and diagnose the patient.