Detection of biomarkers using terahertz metasurface sensors and machine learning

太赫兹辐射 频域 支持向量机 计算机科学 噪音(视频) 生物标志物 人工智能 傅里叶变换 时域 光学 机器学习 算法 物理 化学 计算机视觉 量子力学 生物化学 图像(数学)
作者
Shangjun Lin,Jie Chen,Wentao Liu,Zhenyun Peng,Zhencheng Chen,Fangrong Hu
出处
期刊:Applied Optics [The Optical Society]
卷期号:62 (4): 1027-1027 被引量:4
标识
DOI:10.1364/ao.478461
摘要

To achieve classification and concentration detection of cancer biomarkers, we propose a method that combines terahertz (THz) spectroscopy, metasurface sensors, and machine learning. A metasurface sensor suitable for biomarker detection was designed and fabricated with five resonance frequencies in the range of 0.3–0.9 THz. We collected biomarkers of five types and nine concentrations at 100 sets of time-domain spectra per concentration. The spectrum is processed by noise reduction and fast Fourier transform to obtain the frequency-domain spectrum. Five machine learning algorithms are used to analyze time- and frequency-domain spectra and ascertain which algorithm is more suitable for the classification of the biomarker THz spectrum. Experimental results show that random forest can better distinguish five biomarkers with an accuracy of 0.984 for the time-domain spectrum. For the frequency-domain spectrum, the support vector machine performs better, with an accuracy of 0.989. For biomarkers at different concentrations, we used linear regression to fit the relationship between biomarker concentration and frequency shift. Experimental results show that machine learning can distinguish different biomarker species and their concentrations by the THz spectrum. This work provides an idea and data processing method for the application of THz technology in biomedical detection.
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