使用基于物联网的电子鼻子系统实验室原型用于呼吸传感应用的VOCs的分类和预测
Nikhil Vadera1, Saakshi Dhanekar1,2
1Interdisciplinary Research Division Smart HealthCare, Indian Institute of Technology Jodhpur, Jodhpur 342030, India.
ACS sensors
|January 24, 2025
概括
这项研究开发了一种使用机器学习的电子鼻子 (e-nose) 系统,用于分析呼吸中的挥发性有机化合物 (VOC). 电子鼻子准确地识别了模拟的酒精呼吸样本,为疾病诊断铺平了道路.
科学领域:
- 分析化学 分析化学
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
背景情况:
- 电子鼻子 (e-nose) 系统将气体传感器与机器学习 (ML) 集成,用于呼吸分析.
- 分析呼吸中的挥发性有机化合物 (VOC) 对于诊断至关重要.
研究的目的:
- 开发和验证电子鼻子系统,用于分类呼吸样本中的VOC.
- 为了比较不同ML算法的性能,用于电子鼻子数据分析.
- 为了展示一个便携式,物联网 (IoT) 支持的呼吸分析原型.
主要方法:
- 气体传感器暴露在六种不同的VOC中,记录电压变化.
- 应用ML算法来分析传感器数据用于VOC预测和分类.
- 使用F1得分,准确性和测试时间来比较算法性能.
- 在Google Colab上测试算法和用于物联网模拟的单板计算机.
主要成果:
- 额外树木ML模型在对模拟的酒精呼吸样本进行分类时实现了0.96的准确性.
- 开发的物联网支持的原型成功地区分了正常的呼吸,酒精,薄荷,口水和红.
- 系统评估了算法性能,以确定其在便携式传感器系统中的潜在用途.
结论:
- 开发的电子鼻子系统在呼吸样品分类方面表现出高准确度.
- 这项技术有可能在呼吸诊断中进行可扩展的应用,用于疾病检测和生理监测.
- 该研究强调了ML在增强现实应用中的e-nose能力方面的有效性.
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