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TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra
ACS Sensors ( IF 8.2 ) Pub Date : 2023-02-23 , DOI: 10.1021/acssensors.2c02615 M Arshad Zahangir Chowdhury 1 , Timothy E Rice 1 , Matthew A Oehlschlaeger 1
ACS Sensors ( IF 8.2 ) Pub Date : 2023-02-23 , DOI: 10.1021/acssensors.2c02615 M Arshad Zahangir Chowdhury 1 , Timothy E Rice 1 , Matthew A Oehlschlaeger 1
Affiliation
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The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220–330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors.
中文翻译：
TSMC-Net：使用太赫兹吸收光谱的深度学习多气体分类
从复杂的多组分吸收光谱中识别气体混合物形态是气体传感中的一个问题，可以使用机器学习方法来解决。在这里，我们报告了使用太赫兹 (THz) 吸收光谱、THz 光谱混合分类器网络或 TSMC-Net 进行多气体分类的深度卷积神经网络。TSMC-Net 已开发用于根据 220–330 GHz 频率范围内的指纹旋转吸收光谱识别混合物中的八种挥发性有机化合物。为模型开发准备了一个数据集，该数据集由随机生成的混合物的模拟吸收光谱组成，吸收大于代表可检测极限的阈值并用多个标签注释。有监督的多标签分类问题，即 混合物中单个气体的识别，通过标签功率集转换转换为有监督的多类分类问题。经过训练的模型针对气体混合物的模拟光谱进行了验证和测试，有无高斯白噪声。经过训练的模型对每种纯化合物都表现出高精度、召回率和准确性。类激活图说明了模型的复杂决策过程，并突出显示了识别独特混合物所需的相关频率区域。最后，根据使用基于微电子学的太赫兹吸收光谱仪获得的纯物质和混合物的测量太赫兹吸收光谱证明了该模型。数据集生成策略和深度卷积神经网络方法被推广并且可以外推到其他光谱类型，
更新日期：2023-02-23
中文翻译：
TSMC-Net：使用太赫兹吸收光谱的深度学习多气体分类
从复杂的多组分吸收光谱中识别气体混合物形态是气体传感中的一个问题，可以使用机器学习方法来解决。在这里，我们报告了使用太赫兹 (THz) 吸收光谱、THz 光谱混合分类器网络或 TSMC-Net 进行多气体分类的深度卷积神经网络。TSMC-Net 已开发用于根据 220–330 GHz 频率范围内的指纹旋转吸收光谱识别混合物中的八种挥发性有机化合物。为模型开发准备了一个数据集，该数据集由随机生成的混合物的模拟吸收光谱组成，吸收大于代表可检测极限的阈值并用多个标签注释。有监督的多标签分类问题，即 混合物中单个气体的识别，通过标签功率集转换转换为有监督的多类分类问题。经过训练的模型针对气体混合物的模拟光谱进行了验证和测试，有无高斯白噪声。经过训练的模型对每种纯化合物都表现出高精度、召回率和准确性。类激活图说明了模型的复杂决策过程，并突出显示了识别独特混合物所需的相关频率区域。最后，根据使用基于微电子学的太赫兹吸收光谱仪获得的纯物质和混合物的测量太赫兹吸收光谱证明了该模型。数据集生成策略和深度卷积神经网络方法被推广并且可以外推到其他光谱类型，
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