Machine learning (ML) methods are implemented to classify rotational absorption spectra for gas-phase compounds in the THz region, specifically 220–330 GHz where experimental data is available. Eight ML methods were trained in both standard and one-versus-rest (OVR) implementations using simulated absorption spectra for 12 volatile organic compounds and halogenated hydrocarbons of interest in industrial and environmental gas sensing applications. The performance of the resulting ML classifiers was compared against simulated training spectra in both a 70–30 training–testing split and in tenfold cross-validation studies, with the classifiers exhibiting accuracies in the range of 88–99% for simulated spectra. The classifiers were then tested for their ability to classify noisy experimental rotational spectra for methanol, ethanol, formic acid, acetaldehyde, acetonitrile, and chloromethane. The OVR implementations of the support vector machine (SVM) classifier with both linear and radial basis function kernels and the multi-layer perceptron (MLP) classifier achieved average classification accuracies of 87–94% for the experimental dataset. The study shows that THz spectra in the present frequency region provide a sufficient spectral fingerprint for ML classifiers to learn and predict speciation, allowing automated gas sensing. The present methods can be extrapolated to different frequency ranges and compounds and conditions.

实施了机器学习（ML）方法以对THz区域（尤其是可提供实验数据的220-330 GHz）中气相化合物的旋转吸收光谱进行分类。在工业和环境气体传感应用中，使用了12种挥发性有机化合物和感兴趣的卤代烃的模拟吸收光谱，对8种ML方法进行了标准和一次性比（OVR）实施方式的培训。在70–30个训练测试拆分和十倍交叉验证研究中，将所得的ML分类器的性能与模拟训练光谱进行了比较，分类器显示的模拟光谱精度在88–99％范围内。然后测试分类器对甲醇，乙醇，甲酸，乙醛，乙腈和氯甲烷。支持向量机（SVM）分类器同时具有线性和径向基函数核以及多层感知器（MLP）分类器的OVR实现对实验数据集实现了87-94％的平均分类精度。研究表明，当前频率范围内的太赫兹光谱为ML分类器学习和预测物种形成提供了足够的光谱指纹，从而实现了自动气体感测。可以将本方法外推至不同的频率范围以及化合物和条件。支持向量机（SVM）分类器同时具有线性和径向基函数核以及多层感知器（MLP）分类器的OVR实现对实验数据集实现了87-94％的平均分类精度。研究表明，当前频率范围内的太赫兹光谱为ML分类器学习和预测物种形成提供了足够的光谱指纹，从而实现了自动气体感测。可以将本方法外推至不同的频率范围以及化合物和条件。支持向量机（SVM）分类器同时具有线性和径向基函数核以及多层感知器（MLP）分类器的OVR实现对实验数据集实现了87-94％的平均分类精度。研究表明，当前频率范围内的太赫兹光谱为ML分类器学习和预测物种形成提供了足够的光谱指纹，从而实现了自动气体感测。可以将本方法外推至不同的频率范围以及化合物和条件。