Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.,Science of the Total Environment

当前位置： X-MOL 学术 › Sci. Total Environ. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.
Science of the Total Environment ( IF 8.2 ) Pub Date : 2020-04-04 , DOI: 10.1016/j.scitotenv.2020.138477
Shixiang Yu ₁ , Hanfei Li ₂ , Xin Li ₁ , Yu Vincent Fu ₃ , Fanghua Liu ₄

Affiliation

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, and Bacillus 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 by G were similar to the actual spectra, and D was 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.

中文翻译：

通过拉曼光谱结合生成对抗网络对病原体进行分类。

快速鉴定海洋病原体在海洋生态学中非常重要。人工智能与拉曼光谱技术相结合，由于其快速性和高效率，是鉴定海洋病原体的有前途的选择。然而，考虑到样品收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。在本文中，我们提出了一种通过拉曼光谱结合生成对抗网络（GAN）对海洋病原体进行分类的新方法。培养了三种海洋菌株，即人葡萄球菌，溶藻弧菌和地衣芽孢杆菌。使用拉曼光谱法，我们获得了每个菌株的100个光谱，并将它们装配到GAN模型中进行训练。30岁以后 000次训练迭代，G生成的光谱与实际光谱相似，而D用于测试光谱的准确性。我们的结果表明，我们的方法不仅提高了机器学习分类的准确性，而且还解决了需要大量训练数据的问题。此外，我们已经尝试在拉曼光谱中找到潜在的识别区域，可在该领域的后续相关工作中用作参考。因此，该方法作为病原体鉴定工具具有巨大的潜力。我们的结果表明，我们的方法不仅提高了机器学习分类的准确性，而且还解决了需要大量训练数据的问题。此外，我们已经尝试在拉曼光谱中找到潜在的识别区域，可在该领域的后续相关工作中用作参考。因此，该方法作为病原体鉴定工具具有巨大的发展潜力。我们的结果表明，我们的方法不仅提高了机器学习分类的准确性，而且还解决了需要大量训练数据的问题。此外，我们已经尝试在拉曼光谱中找到潜在的识别区域，可在该领域的后续相关工作中用作参考。因此，该方法作为病原体鉴定工具具有巨大的发展潜力。

更新日期：2020-04-06

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11