Nguyen Ngan Anh, Hendricks Asher, Montoya Emily, Mayers Amber, Rajmohan Diwitha, Morrin Aoife, McCaul Margaret, Dunne Nicholas, O'Connor Noel, Spanias Andreas, Raupp Gregory, Forzani Erica
School of Engineering for Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, USA.
Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.
Sensors (Basel). 2025 Jul 29;25(15):4693. doi: 10.3390/s25154693.
Blood iron levels are related to many health conditions, affecting hundreds of millions of individuals worldwide. To aid in the prevention and treatment of iron-related disorders, previous research has developed a low-cost, accurate, point-of-care method for measuring iron from a single finger-prick blood sample. This study builds upon that work by introducing an improved imaging method that accurately reads sensor images irrespective of variations in environmental illumination and camera quality. Smartphone cameras were used as analytical tools, demonstrating an average coefficient of variation of 5.13% across different phone models, and absorbance results were found to be improved by 8.80% compared to the method in a previous study. The proposed method successfully enhances iron detection accuracy under diverse lighting conditions, paving the way for smartphone-based sensing of other colorimetric reactions involving various analytes.
血液铁水平与许多健康状况相关,影响着全球数亿人。为了帮助预防和治疗与铁相关的疾病,先前的研究开发了一种低成本、准确的即时检测方法,用于从单指血样中测量铁。本研究在此基础上进行,引入了一种改进的成像方法,该方法能够准确读取传感器图像,而不受环境光照和相机质量变化的影响。使用智能手机相机作为分析工具,不同手机型号的平均变异系数为5.13%,与先前研究中的方法相比,吸光度结果提高了8.80%。所提出的方法成功提高了在不同光照条件下的铁检测准确性,为基于智能手机的其他涉及各种分析物的比色反应传感铺平了道路。