Deep Neural Network-Assisted Microfluidic pH Sensor

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-03-13 DOI:10.1109/JSEN.2025.3548912

Henry E. Ventura-Grandez;Jonathan Quevedo;Itamar Salazar-Reque;Maria Armas-Alvarado;Luz Adanaque-Infante;Ruth Rubio-Noriega

引用次数: 0

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.

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深度神经网络辅助微流体pH传感器

水的pH值测量是至关重要的，因为它提供了有关其质量和适合农业、水生生态系统、工业和人类消费的基本信息。这些应用都可能需要酸度或碱度的数值读数，最好使用已经无处不在的工具，比如手机。本工作提出了一种微流控芯片实验室系统来测量液体样品的pH值。我们使用紫甘蓝作为比色试剂，在聚二甲基硅氧烷（PDMS）微流体受体上生成了一个pH值在[2-12]范围内的2640张图像数据集。我们将数据集输入参数化深度神经网络（DNN）对样本进行分类，发现准确率为99.7%。此外，我们开发了一个移动应用程序，具有易于使用的图形用户界面，可以识别微流体装置的形状，分类图像的颜色，并返回pH值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice