Karademir Turgut, Kaleli-Can Gizem, Köktürk-Güzel Başak Esin
Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir Demokrasi University, 35140 Izmir, Türkiye.
Department of Biomedical Engineering, Faculty of Engineering, Izmir Demokrasi University, 35140 Izmir, Türkiye.
Biosensors (Basel). 2025 Aug 5;15(8):507. doi: 10.3390/bios15080507.
Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform-using CA19-9 as a model biomarker-that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications.
基于纸的比色生物传感器是一类很有前景的低成本诊断工具，无需外部仪器。然而，它们的更广泛应用受到与传统金属基纳米材料相关的环境问题以及视觉解读主观性的限制。为应对这些挑战，本研究引入了一个概念验证平台——以CA19-9作为模型生物标志物——该平台将天然衍生的黑色素纳米颗粒（MNP）与基于机器学习的图像分析相结合，以实现环境可持续且分析稳健的比色定量。在与目标结合后，MNP会引发从黄色到棕色的浓度依赖性颜色转变。使用包含自动区域分割和回归建模的机器学习流程对这个视觉信号进行了量化。使用三种不同算法对传感器区域进行分割，其中U-Net模型实现了最高的准确率（平均交并比：0.9025±0.0392）。从分割区域提取的特征用于训练七个回归模型，其中XGBoost表现最佳，平均绝对百分比误差（MAPE）为17%。尽管由于传感器饱和，在较高分析物浓度下观察到灵敏度降低，但该模型在较低浓度下显示出很强的预测准确性，而这对于视觉解读尤其具有挑战性。这种方法能够对比色信号进行准确、可重复且客观的定量，从而为即时诊断应用提供了一种可持续且可扩展的替代方案。
