Classification of pathogens by Raman spectroscopy combined with generative adversarial networks
Classification of pathogens by Raman spectroscopy combined with generative adversarial networks
复制标题
拉曼光谱结合生成对抗网络对病原体进行分类
DOI：
10.1016/j.scitotenv.2020.138477
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发表时间：
2020
影响因子：
9.8
通讯作者：
Liu Fanghua
中科院分区：
文献类型：
--
作者：
Yu Shixiang;Li Hanfei;Li Xin;Fu Yu Vincent;Liu Fanghua
海洋病原体的快速鉴定在海洋生态学中具有十分重要的意义。人工智能与拉曼光谱相结合，以其快速、高效的特点成为海洋病原体识别的一种有前途的选择。然而，考虑到样品采集的成本和实验环境的挑战性，通常只有有限的光谱可用于建立分类模型，这阻碍了定性分析。本文提出了一种基于拉曼光谱和产生式对抗网络相结合的海洋病原体分类新方法。对人葡萄球菌、溶藻弧菌和地衣芽孢杆菌三株海洋菌株进行了培养。利用拉曼光谱，我们获得了每个菌株的100个光谱，并将它们拟合到GaN模型中进行训练。经过30,000次训练迭代，G生成的光谱与实际光谱相似，并用D来检验光谱的准确性。实验结果表明，该方法不仅提高了机器学习分类的准确率，而且解决了需要大量训练数据的问题。此外，我们还试图在拉曼光谱中寻找潜在的识别区域，以供该领域后续的相关工作参考。因此，该方法具有作为病原体鉴定工具的巨大潜力。
Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely,Staphylococcus hominis,Vibrio alginolyticus, andBacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated byGwere similar to the actual spectra, andDwas used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification.