Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning
文献类型：期刊论文
| 作者 | B. Liu; K. Liu; J. Sun; L. Shang; Q. Yang; X. Chen and B. Li |
| 出版日期 | 2022 |
| ISSN号 | 1864063X |
| DOI | 10.1002/jbio.202200270 |
| 英文摘要 | Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the "fingerprint" of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified. 2022 Wiley-VCH GmbH.￥internal-pdf://2896258867/Classification of pathogenic bacteria by Raman.pdf￥10.1002/jbio.202200270￥https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jbio.202200270?download=true￥ei |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/66469]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | B. Liu,K. Liu,J. Sun,et al. Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning[J],2022. |
| APA | B. Liu,K. Liu,J. Sun,L. Shang,Q. Yang,&X. Chen and B. Li.(2022).Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning.. |
| MLA | B. Liu,et al."Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning".(2022). |
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