Abstract
A combined thread-paper microfluidic device (μTPAD) is presented for the determination of glucose in blood. The device is designed to include all the analytical operations needed: red blood cell separation, conditioning, enzymatic recognition, and colorimetric transduction. The signal is captured with a smartphone or tablet working in video mode and processed by custom Android-based software in real-time. The automatic detection of the region of interest on the thread allows for the use of either initial rate or equilibrium signal as analytical parameters. The time needed for analysis is 12 s using initial rate, and 100 s using the equilibrium measurement with a LOD of 48 μM and 12 μM, respectively, and a precision around 7%. The μTPAD allows a rapid determination of glucose in real samples using only 3 μL of whole blood.
Highlights
Two versions of image processing software were written for use with this sensor
The primary difference between the versions is that the Android app is designed to run natively on a variety of smartphones in real-time where the PC version is designed to work with recorded videos from any recorded source and to provide diagnostic data useful in the development of the devices
The OpenCV library function cvtColor was used to convert from the RGB color space to the HSV color space
Summary
Two versions of image processing software were written for use with this sensor. The PC based version was written using the Anaconda distribution of Python 2.7 (https://www.anaconda.com/) in conjunction with the Python/Windows interface to the Open Source Computer Vision Library version 3.2.0 (https://opencv.org/). The histograms of RGB, HSV, and absorbance ratios for pixels in the region of interest were calculated using the calcHist function and displayed using the functions normalize and line by drawing directly onto the displayed image. While the video is being analyzed, the mean and standard deviation of all color values (RGB, HSV, and cA123) of pixels inside the region of interest for a single video frame are displayed on the screen and recorded in a text file along with the elapsed time for that frame (hereafter referred to as frame means). The slope of the best fit line was taken to be the rate of change in the sensor response Both the moving average of the analytical parameter and the rate of change are displayed as separate auto-scaling plots in the lower right of the display.
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