In recent decades, terahertz (THz) radiation has been widely applied in many chemical and biomedical areas. Due to its ability to resolve the absorption features of many compounds noninvasively, it is a promising technique for chemical recognition of substances such as drugs or explosives. A key challenge for THz technology is to be able to accurately classify spectral measurements acquired in unknown complicated environments, rather than those from ideal laboratory conditions. Support vector machine (SVM) and deep neural networks (DNNs) are powerful and widely adopted approaches for complex classification with a high accuracy. In this article, we explore and apply the SVM and DNN methods for classifying the frequency spectra of glucose and lactose. We measured 372 groups of independent signals under different conditions to provide a sufficient training set. The classification accuracies achieved were 99% for the SVM method and 89.6% for the DNN method. These high classification accuracies demonstrate great potential in chemical recognition.

中文翻译：

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基于 SVM 和 DNN 方法的葡萄糖和乳糖太赫兹光谱分类

近几十年来，太赫兹（THz）辐射已广泛应用于许多化学和生物医学领域。由于它能够无创地解析许多化合物的吸收特征，因此它是一种很有前途的化学识别物质，如药物或爆炸物的技术。太赫兹技术的一个关键挑战是能够准确地对未知复杂环境中获得的光谱测量进行分类，而不是来自理想实验室条件的光谱测量。支持向量机 (SVM) 和深度神经网络 (DNN) 是用于高精度复杂分类的强大且广泛采用的方法。在本文中，我们探索并应用 SVM 和 DNN 方法对葡萄糖和乳糖的频谱进行分类。我们在不同条件下测量了 372 组独立信号，以提供足够的训练集。SVM 方法获得的分类准确率为 99%，DNN 方法为 89.6%。这些高分类精度在化学识别方面展示了巨大的潜力。