Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, Guilin 541004, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China; Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China.
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Oct 15;279:121313. doi: 10.1016/j.saa.2022.121313. Epub 2022 Apr 30.
This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3-1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition-particle swarm optimization-support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.
本研究探讨了太赫兹（THz）技术在快速识别同分异构体方面的可行性。使用太赫兹时域光谱系统（THz-TDS）在 0.3-1.8 THz 范围内测量了 2-羟基苯甲酸（2-HA）、3-羟基苯甲酸（3-HA）和 4-羟基苯甲酸（4-HA）的时域光谱。针对同分异构体分类问题，提出了一种基于变分模态分解-粒子群优化-支持向量机（VMD-PSO-SVM）方法的太赫兹光谱数据分类模型。经验模态分解（EMD）和变分模态分解（VMD）用于提取时域信号的前八个固有模态函数（IMF）。主成分分析（PCA）用于提取每个模态分量的前 80 个主成分作为分类特征向量。粒子群优化（PSO）和支持向量机（SVM）算法用于构建 2-HA、3-HA 和 4-HA 分类模型。我们发现，无论模态分量如何，VMD-PSO-SVM 模型的预测精度都明显高于 EMD-PSO-SVM 模型。对于 EMD 和 VMD 两种方法，随着 IMF 数量的增加，相应的分类识别精度趋于降低。结果表明，基于太赫兹光谱和 SVM 的羟基苯甲酸同分异构体快速识别模型是有效且可行的，为生物医药的化学合成和质量监测提供了一种准确、快速的方法。
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