Deep Neural Network-Assisted Microfluidic pH Sensor

Abstract

Water pH measurement is vital as it provides fundamental information about its quality and suitability for agriculture, aquatic ecosystems, industry, and human consumption. Each of these applications may require numerical readings of acidity or alkalinity, preferably using tools that are already ubiquitous, such as cellphones. This work presents a microfluidic lab-on-a-chip system to measure the pH of liquid samples. We used purple cabbage as the colorimetric reagent to produce a 2640-image dataset with pH levels in the range of [2–12] on a polydimethylsiloxane (PDMS) microfluidic recipient. We fed our dataset to our parameterized deep neural network (DNN) to classify our samples and found an accuracy of 99.7%. In addition, we developed a mobile application with an easy-to-use graphic user interface that recognizes the microfluidic device shape, classifies the image’s color, and returns the pH level.