Single board computing system for automated colorimetric analysis on low-cost analytical devices
Colorimetric detection, while a user-friendly and easily implemented method of analysis on low-cost analytical devices, often suffers from subjectivity by the device user. We describe the development of a single board computing system to automatically analyze colorimetric samples. Single board computing systems, such as Raspberry Pi, offer significant computing power in a small, inexpensive package. By programming an attached camera to obtain images every minute, we demonstrated that Michaelis–Menten enzyme kinetic constants can be calculated directly from paper and transparency devices based on the change in color intensity over time in seconds instead of hours using an integrated image analysis program. In this system, a 3D-printed box was designed and optimized with an independent lighting system that holds the paper-based devices and the Raspberry Pi board and camera. The box omits environmental and ambient light for consistent lighting and holds the camera at a constant focal length. While early versions of the image analysis program used single pixels, the final program uses a flood fill algorithm for colorimetric analysis so the system is not restricted by device shape and can discriminate discoloration due to lighting, making it adaptable to other colorimetric device applications. As a proof-of-principle, we compared enzyme kinetics between Whatman chromatography paper and transparency-based devices and found that changing the platform did not compromise the apparent Vmax and KM calculated by the program.