Analysis and Research on Terahertz Spectroscopy in Machine Learning

This research paper focuses on the analysis and research of terahertz spectroscopy in machine learning. Terahertz technology, which encompasses electromagnetic waves with frequencies ranging from 0.1 to 10 THz, has gained significant attention due to its various applications in fields like astronomy, biomedical research, and communication. Traditional terahertz spectroscopy techniques face limitations such as damaging the original material structure, low sensitivity, and complex operations. Therefore, this study aims to explore qualitative analysis methods for substances using terahertz time-domain spectroscopy technology coupled with machine learning algorithms. Machine learning, specifically supervised learning, plays a crucial role in this research. By leveraging the power of machine learning algorithms, the study aims to develop models with higher prediction accuracy for recognizing and classifying different substances. The potential applications of machine learning in areas such as food safety, drug detection, and biomedical research are also highlighted. Theoretical foundations of terahertz time-domain spectroscopy, sample preparation, and spectral acquisition methods are discussed in the paper. The study utilizes a transmission-type terahertz time-domain spectroscopy system with a femtosecond fiber laser as the light source for measuring terahertz spectra. The paper also explains the concept of supervised learning and introduces the K-nearest neighbors (KNN) algorithm as a classification approach. To evaluate the classification performance, the paper presents classification evaluation metrics such as F1 scores. Experimental results demonstrate the effectiveness of the proposed approach, showcasing high recognition rates and classification performance for different substances.