Xie Yuhao, Wang Zilong, Chen Qiang, Tang Heshan, Huang Jie, Liang Pei
College of Optical and Electronic Technology, China Jiliang University, 310018, Hangzhou, China.
Xiamen Palantier Technology Co., Ltd, Xiamen, 361115, China.
Anal Methods. 2024 Aug 29;16(34):5793-5801. doi: 10.1039/d4ay00602j.
Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.
拉曼光谱法被广泛用于物质识别，它能提供来自各种成分的分子信息，但同时也伴随着噪声和仪器干扰。因此，基于拉曼光谱识别成分仍然具有挑战性。在本研究中，我们使用便携式拉曼光谱仪收集了474种危险化学物质的拉曼光谱数据，得到了一个包含59468个光谱的数据集。我们的研究采用了基于ResNet架构的深度神经卷积网络，并结合了一种名为SE模块的注意力机制。通过增强某些光谱特征的权重，模型的性能得到了显著提升。我们还研究了该模型在小样本条件下的分类预测性能，以便于添加新的危险化学类别，供未来在移动设备上部署。此外，我们从拉曼强度和拉曼位移两个方面探索了卷积神经网络从拉曼光谱中提取的特征。我们发现，神经网络在物质分类时并非仅依赖强度或位移，而是有效地将这两个方面结合起来。本研究有助于推动拉曼光谱法在危险化学物质识别方面的应用发展，特别是在数据可用性有限的场景中。这些发现揭示了光谱特征在模型决策过程中的重要性，并对深度学习技术在基于拉曼光谱的物质识别中的更广泛应用具有启示意义。
