Cai Yaoyi, Yao Zekai, Cheng Xi, He Yixuan, Li Shiwen, Pan Jiaji
College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China; Xiangji Haidun Technology Co., Ltd., Changsha, Hunan 410199, PR China.
College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 15;303:123085. doi: 10.1016/j.saa.2023.123085. Epub 2023 Jun 30.
Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification.
使用便携式或手持式拉曼光谱检测设备快速识别未知物质样本正成为一种常见的分析工具。然而，一套基于拉曼光谱的物质识别设备的设计与实现必须包括标准参考物质样本的光谱采样、不同设备之间的分辨率匹配以及相应分类模型的训练过程。选择合适的分类模型的过程通常很耗时，并且当要识别的物质类别数量急剧增加时，识别准确率会大幅下降。在本文中，我们提出了一种基于深度度量学习网络并结合格拉姆角差场（GADF）图像生成方法的拉曼光谱快速分类方法。首先，我们将在不同分辨率下采集的拉曼光谱统一转换为相同分辨率的GADF图像，解决了不同拉曼光谱检测设备分辨率差异引起的光谱维度差异问题。其次，基于深度度量学习方法设计了一个能够对不同类别的物质的GADF图像进行非线性距离测量的网络。从RRUFF数据库中获取的450种不同矿物类别的拉曼光谱被转换为GADF图像，并用于训练这个深度度量学习网络。最后，训练好的网络可以安装在嵌入式计算平台上，并与便携式或手持式拉曼光谱检测传感器结合使用，以执行各种规模的物质识别任务。一系列实验表明，我们训练的深度度量学习网络在不同规模的分类任务上优于现有的主流机器学习模型。对于260类天然矿物的拉曼光谱分类和8类有显著噪声的病原菌的拉曼光谱分类这两个任务，我们提出的模型分别达到了98.05%和90.13%的分类准确率。最后，我们还将该模型部署在手持式拉曼光谱仪中，并对属于32类的350个化学物质样本进行了识别实验，分类准确率达到了99.14%。这些结果表明，我们的方法可以大大提高基于拉曼光谱的物质检测设备的开发效率，并可广泛应用于未知物质识别任务。
