Smartphone Platform for Multisignal Parallel Detection, Processing, and Visualization in Fluorescence Biosensing

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

IEEE Sensors Journal Pub Date : 2025-04-11 DOI:10.1109/JSEN.2025.3558238

Weihe Zhan;Qingfubo Geng;Baole Wang;Yian Liu;Zhaoxin Geng

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引用次数: 0

Abstract

The fluorescence sensing method is widely used in biochemical sensing. With the development of hardware structures and software systems in smartphones, this method has gradually evolved from large laboratory equipment to smartphones. However, current methods mainly rely on single signal processing and provide few detection schemes, requiring greater flexibility and efficiency. To overcome these challenges, a new smartphone platform is proposed for signal processing in fluorescence biosensing experiments. The platform supports multisignal parallel processing and precise target localization by integrating multiple image preprocessing algorithms and a Hough transform-assisted grayscale distribution algorithm. In addition, the platform provides multiple methods for sampling and concentration curve fitting, ensuring the flexibility and verifiability of experimental design. The experimental results demonstrate that the localization algorithm achieves a hit rate of 99.48%, with an average deviation distance of only 3.595 pixels. The minimum mean square error (mse) between the average pixel value of the target signal area calculated by the platform and the actual pixel value is only 3.158, and the minimum mean absolute error (MAE) is only 1.360. Validation experiments further confirm that the sensitivity and accuracy of the platform meet the basic requirements for fluorescence sensing detection. This platform has broad applicability in biomedical engineering and point-of-care testing (POCT), improving portable diagnostic tools and paving the way for home biochemical sensing applications, such as early cancer screening and cardiovascular disease prevention.

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荧光生物传感中多信号并行检测、处理和可视化的智能手机平台

荧光传感方法在生物化学传感中有着广泛的应用。随着智能手机硬件结构和软件系统的发展，这种方法逐渐从大型实验室设备发展到智能手机。然而，目前的方法主要依赖于单信号处理，提供的检测方案很少，需要更大的灵活性和效率。为了克服这些挑战，提出了一种新的智能手机平台，用于荧光生物传感实验中的信号处理。该平台通过集成多种图像预处理算法和Hough变换辅助灰度分布算法，支持多信号并行处理和精确目标定位。此外，该平台还提供了多种采样和浓度曲线拟合方法，保证了实验设计的灵活性和可验证性。实验结果表明，该定位算法的准确率为99.48%，平均偏差距离仅为3.595像素。平台计算的目标信号区域平均像素值与实际像素值之间的最小均方误差（mse）仅为3.158，最小平均绝对误差（MAE）仅为1.360。验证实验进一步证实，该平台的灵敏度和精度满足荧光传感检测的基本要求。该平台在生物医学工程和护理点检测（POCT）中具有广泛的适用性，改进了便携式诊断工具，为早期癌症筛查和心血管疾病预防等家庭生化传感应用铺平了道路。

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