机器学习辅助的柔性可穿戴设备用于氨酸检测
Qiwen Bao1, Gang Li1, Wenbo Cheng2
1School of Precision Instrument and Optoelectronic Engineering, The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University 92 Weijin Road Tianjin 300072 China linling@tju.edu.cn.
RSC advances
|August 10, 2023
概括
这项研究引入了一个智能,便携式的电化学生物传感器来检测氨酸 (Tyr). 机器学习通过将传感器数据与pH和温度等环境因素相结合来提高准确性,从而改善疾病诊断.
科学领域:
- 电化学 电化学 电化学
- 生物传感器是一种生物传感器.
- 机器学习 机器学习
- 分析化学 分析化学
背景情况:
- 早期诊断病理标志物对于疾病控制至关重要.
- 目前的临床方法通常是昂贵和复杂的.
- 需要更简单,更快速,更聪明的分析技术.
研究的目的:
- 开发一个智能,便携式的电化学生物传感器来检测氨酸.
- 利用机器学习来提高分析准确度.
- 将一个灵活的可穿戴设备与一种新型电极材料集成在一起.
主要方法:
- 一个碳黑-石墨烯氧化物联合聚合物 (CB-GO/CP) 电极的制造.
- 开发一种灵活的可穿戴设备,用于便携式检测.
- 人工神经网络 (ANN) 和支持矢量机器 (SVM) 模型的应用.
- 在人工尿中收集数据,包括pH值和温度变化.
主要成果:
- 拟议的CB-GO/CP电极展示了有效的电化学传感能力.
- 机器学习模型准确地预测了氨酸度.
- 外部因素 (pH,温度) 的整合显著提高了模型预测的准确性.
结论:
- 机器学习辅助的电化学生物传感器为智能,便携式诊断提供了一个有希望的方法.
- 开发的系统为传统方法提供了更简单,更高效和更智能的替代方案.
- 这项技术在早期疾病诊断和监测方面具有潜在的应用.
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