VOC-Net: A Deep Learning Model for the Automated Classification of Rotational THz Spectra of Volatile Organic Compounds
VOC-Net: A Deep Learning Model for the Automated Classification of Rotational THz Spectra of Volatile Organic Compounds
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DOI：
10.3390/app12178447
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
M. Chowdhury;T. Rice;M. Oehlschlaeger
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文献类型：
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作者：
M. Chowdhury;T. Rice;M. Oehlschlaeger
传统的黑匣子机器学习（ML）算法已经在文献中报道了从太赫兹频率区域吸收光谱中识别气相物种的方法。虽然这种机器学习模型的鲁棒分类性能很有希望，但这些机器学习工具的黑箱性质限制了它们在应用中的可解释性和可接受性。在这里，一维卷积神经网络（CNN）， VOC-Net，被开发和演示用于太赫兹频率范围内挥发性有机化合物（VOCs）的吸收光谱分类，特别是从220到330 GHz，其中先前的实验数据是可用的。vocs - net对模拟光谱进行了训练和验证，并对实验光谱进行了演示和测试。通过考虑混淆矩阵和接收机-算子-特征（ROC）曲线来检验VOC-Net的性能。该模型对模拟光谱的分类准确率为99%以上，对有噪声的实验光谱的分类准确率为97%。使用梯度加权类激活映射（Grad-CAM）方法检查模型的内部逻辑，该方法提供了关于重要区分光谱特征的模型决策过程的可视化和可解释的解释。
Conventional black box machine learning (ML) algorithms for gas-phase species identification from THz frequency region absorption spectra have been reported in the literature. While the robust classification performance of such ML models is promising, the black box nature of these ML tools limits their interpretability and acceptance in application. Here, a one-dimensional convolutional neural network (CNN), VOC-Net, is developed and demonstrated for the classification of absorption spectra for volatile organic compounds (VOCs) in the THz frequency range, specifically from 220 to 330 GHz where prior experimental data is available. VOC-Net is trained and validated against simulated spectra, and also demonstrated and tested against experimental spectra. The performance of VOC-Net is examined by the consideration of confusion matrices and receiver-operator-characteristic (ROC) curves. The model is shown to be 99+% accurate for the classification of simulated spectra and 97% accurate for the classification of noisy experimental spectra. The model’s internal logic is examined using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, which provides a visual and interpretable explanation of the model’s decision making process with respect to the important distinguishing spectral features.