一个智能传感平台,用于检测和识别基于太赫兹光谱的生物化学物质
Yusa Chen1, Shisong Xiong1, Meizhang Wu2
1National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Beijing, 100871, PR China; School of Integrated Circuits, Peking University, Beijing, 100871, PR China.
Talanta
|October 1, 2024
概括
这项研究引入了一个智能平台,用于识别使用太赫兹 (THz) 光谱的生物化学物质. 它通过新型迷你卷积神经网络 (MCNN) 和YOLO-v5模型在区分氨基酸和碳水化合物方面实现了高精度.
科学领域:
- 光谱学和传感技术
- 生化分析 生化分析
- 机器学习应用 机器学习应用
背景情况:
- 太赫兹 (THz) 谱学为生物化学物质的非破坏性分析提供了独特的能力.
- 准确识别氨基酸和碳水化合物对于各种科学和工业应用至关重要.
- 开发用于光谱分析的智能平台可以显著提高检测效率和准确性.
研究的目的:
- 开发和评估一个智能传感平台,以基于其THz光谱识别生物化学物质.
- 实现和比较两个不同的识别模式:一维THz频谱识别 (OTSI) 和基于图像的THz频谱识别 (TSII).
- 评估拟议的机器学习模型,迷你卷积神经网络 (MCNN) 和YOLO-v5在光谱识别任务中的性能.
主要方法:
- 使用THz时域光谱法 (THz-TDS) 来检测生化物质.
- 开发了一个小型卷积神经网络 (MCNN) 模型,用于分析一维THz吸收系数.
- 应用了YOLO-v5目标检测模型用于THz光谱图像识别,使用吸收峰值作为特征.
主要成果:
- 该MCNN模型从其1D THz光谱中识别了10种生化物质 (五种氨基酸和五种碳水化合物),达到99.07%的准确性.
- YOLO-v5模型在使用光谱图像识别来识别相同物质时显示了96.20%的准确性.
- 在比较分析中,MCNN和YOLO-v5模型都超过了其他传统的深度学习和机器学习模型.
结论:
- 开发的智能传感平台有效地使用THz光谱识别生化物质.
- MCNN和YOLO-v5模型为THz光谱分析提供了强大而准确的方法.
- 这项研究推进了THz光谱在生物分子分析中的实用性,并为检测各种生物化合物开辟了新的途径.
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