Zou Xue, Wang Xiaohong, Tu Jinchun, Chen Delun, Cao Yang
State Key Laboratory of Marine Resource Utilization in South China Sea, College of Material Science and Engineering, Hainan University, Haikou 570228, China.
School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China.
Biosensors (Basel). 2025 Feb 26;15(3):148. doi: 10.3390/bios15030148.
The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between different mode results is too large. In this study, we propose a multi-mode sensor based on Prussian Blue (PB) for ascorbic acid (AA) detection. We innovatively integrate back-propagation artificial neural networks (BP ANNs) to comprehensively process the three collected signal data sets, which successfully solves the problem of sensor failure caused by the large deviation of signal detection results, and greatly improves the prediction accuracy, detection range, and anti-interference of the sensor. Our findings provide an effective solution for optimizing the data analysis of multi-modal sensors, and show broad application prospects in bioanalysis, clinical diagnosis, and related fields.
小分子的检测在许多领域都至关重要，但传统的电化学检测方法往往准确性有限。构建多模式传感器是提高检测准确性的常用策略。然而，现有的大多数多模式传感器依赖于对每种模式信号的单独分析，当不同模式结果之间的偏差过大时，很容易导致传感器故障。在本研究中，我们提出了一种基于普鲁士蓝（PB）的用于检测抗坏血酸（AA）的多模式传感器。我们创新性地集成了反向传播人工神经网络（BP ANN）来综合处理采集到的三个信号数据集，成功解决了信号检测结果偏差过大导致的传感器故障问题，并大大提高了传感器的预测准确性、检测范围和抗干扰能力。我们的研究结果为优化多模态传感器的数据分析提供了有效解决方案，并在生物分析、临床诊断及相关领域展现出广阔的应用前景。
