用实验与生成数据训练神经网络，实现挥发性有机物的精准识别与浓度预测。
Deep learning recognition and analysis of Volatile Organic Compounds based on experimental and synthetic infrared absorption spectra
- 结合真实与生成红外光谱，构建九类挥发性有机物数据集
- 模型可准确识别九类物质并预测其浓度，支持实时检测
- 适合集成到便携式传感器中，用于环境健康监测
挥发性有机化合物（VOCs）是沸点低、易挥发的有机分子，对人类健康构成重大威胁，因此其精确检测是降低暴露风险的关键。红外（IR）光谱可通过测量VOCs的红外吸收光谱实现大气中低浓度下的超灵敏检测。然而，红外光谱的复杂性限制了其实时识别与定量分析。尽管深度神经网络（NN）常用于复杂数据结构识别，但通常需要大规模数据集进行训练。本文构建了一个包含九类化合物在不同浓度下的实验VOC数据集，利用条件生成神经网络生成合成光谱以扩充数据量和浓度多样性。基于此，训练出稳健的判别型神经网络，能够可靠识别九类VOCs，并精确预测其浓度。该模型可集成至传感设备中，实现VOCs的实时识别与分析。
原文摘要 · Abstract (English)
Volatile Organic Compounds (VOCs) are organic molecules that have low boiling points and therefore easily evaporate into the air. They pose significant risks to human health, making their accurate detection the crux of efforts to monitor and minimize exposure. Infrared (IR) spectroscopy enables the ultrasensitive detection at low-concentrations of VOCs in the atmosphere by measuring their IR absorption spectra. However, the complexity of the IR spectra limits the possibility to implement VOC recognition and quantification in real-time. While deep neural networks (NNs) are increasingly used for the recognition of complex data structures, they typically require massive datasets for the training phase. Here, we create an experimental VOC dataset for nine different classes of compounds at various concentrations, using their IR absorption spectra. To further increase the amount of spectra and their diversity in term of VOC concentration, we augment the experimental dataset with synthetic spectra created via conditional generative NNs. This allows us to train robust discriminative NNs, able to reliably identify the nine VOCs, as well as to precisely predict their concentrations. The trained NN is suitable to be incorporated into sensing devices for VOCs recognition and analysis.
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