RGB颜色模型
计算机科学
标准化
跟踪(教育)
比色法
智能手机应用程序
计算机视觉
HSL和HSV色彩空间
计算机硬件
人工智能
人机交互
医学
心理学
操作系统
病毒学
教育学
病毒
作者
Samira Balbach,Nan Jiang,Rosalia Moreddu,Xingchen Dong,Wolfgang Kurz,Congyan Wang,Jie Dong,Yixia Yin,Haider Butt,Martin Brischwein,Oliver Hayden,Martin Jakobi,Savaş Taşoğlu,Alexander W. Koch,Ali K. Yetisen
出处
期刊:Analytical Methods
[Royal Society of Chemistry]
日期:2021-01-01
卷期号:13 (38): 4361-4369
被引量:70
摘要
Colorimetric tests for at-home health monitoring became popular 50 years ago with the advent of the urinalysis test strips, due to their reduced costs, practicality, and ease of operation. However, developing digital systems that can interface these sensors in an efficient manner remains a challenge. Efforts have been put towards the development of portable optical readout systems, such as smartphones. However, their use in daily settings is still limited by their error-prone nature associated to optical noise from the ambient lighting, and their low sensitivity. Here, a smartphone application (Colourine) to readout colorimetric signals was developed on Android OS and tested on commercial urinalysis test strips for pH, proteins, and glucose detection. The novelty of this approach includes two features: a pre-calibration step where the user is asked to take a photo of the commercial reference chart, and a CIE-RGB-to-HSV color space transformation of the acquired data. These two elements allow the background noise given by environmental lighting to be minimized. The sensors were characterized in the ambient light range 100-400 lx, yielding a reliable output. Readouts were taken from urine strips in buffer solutions of pH (5.0-9.0 units), proteins (0-500 mg dL-1) and glucose (0-1000 mg dL-1), yielding a limit of detection (LOD) of 0.13 units (pH), 7.5 mg dL-1 (proteins) and 22 mg dL-1 (glucose), resulting in an average LOD decrease by about 2.8 fold compared to the visual method.
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