Electrical and Computer Engineering, Rutgers University-New Brunswick, 94 Brett Road, Piscataway, NJ, USA.
Analyst. 2024 Mar 11;149(6):1719-1726. doi: 10.1039/d3an01356a.
Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an -square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.
葡萄糖是诊断和预测各种疾病的重要生物标志物，包括糖尿病和低血糖症，这些疾病会对患者产生严重的副作用、症状，甚至导致死亡。因此，需要快速、经济的血糖水平测量来帮助识别潜在风险的人群。随着智能手机用户的增加，便携式智能手机血糖仪变得越来越受欢迎。在本文中，我们提出了一种一次性微流控葡萄糖传感器，该传感器使用比色分析和计算机视觉相结合的方法，准确快速地定量检测人尿液中的葡萄糖水平。该葡萄糖传感器基于医用胶带和葡萄糖分析条实现了一次性微流控设备，集成在带有定制化聚二甲基硅氧烷（PDMS）微泵的玻璃载玻片上，加速了毛细管流动，使其具有经济、方便、快速和无设备的特点。在吸收目标溶液后，一次性设备被滑入 3D 打印的主底盘中，仅用发光二极管（LED）照明进行照明，这对于颜色敏感实验至关重要。收集图像后，将图像导入算法中，使用计算机视觉和平均 RGB 值测量来测量葡萄糖水平。本文说明了葡萄糖传感器在定量蔗糖水中葡萄糖方面的出色准确性和一致性。这可以从传感器使用的计算机视觉方法与生物学领域传统的测量方法之间的紧密一致性以及不同传感器性能之间观察到的小变化得到证明。研究中使用的指数回归曲线进一步证实了葡萄糖浓度与平均 RGB 值之间的强相关性，-平方值为 0.997 表明这些变量之间具有高度相关性。文章还强调了所描述的解决方案在其他类型的测定和基于智能手机的传感器中的潜在可转移性。
