Abstract：The expeditious identification of pathogenic bacteria plays a prominent role in preventing the spread of infectious diseases, helping combat antimicrobial resistance, and improving patient prognosis. Raman spectroscopy combined with machine learning algorithms can provide simple and fast label-free detection of pathogenic bacteria. However, pathogenic bacteria are diverse and phenotypic. However, deep learning relies on many samples for training, while collecting Raman spectra of large batches of pathogenic bacteria is laborious and vulnerable to factors such as fluorescence. To address the above problems, a pathogenic bacteria Raman spectroscopy detection model based on the combination of the WGAN-GP data enhancement method and ResNet is proposed. Raman spectra of five common ophthalmic pathogenic bacteria were used. The collected raw data are normalized as the input of ResNet and ordinary convolutional neural network (1D-CNN), SG filtering, airPLS baseline correction, PCA data downscaling data preprocessing as the input of K nearest neighbor algorithm (KNN), and the comparative analysis finds that the ResNet model works best and its classification accuracy can reach 96%; build Wasserstein Generative Adversarial Network with Gradient Penalty Model (WGAN-GP) is built to generate a large amount of high-resolution spectral data similar to the real data. In order to verify that the generated data can enrich the data diversity and thus improve the classification accuracy, the expanded dataset was re-entered into the ResNet model for training, and the classification accuracy of WGAN-GP combined with ResNet was finally improved. The classification accuracy of WGAN-GP combined with ResNet was improved to 99.3%. The improved WGAN-GP model is suitable for Raman spectral data enhancement, which solves the problem of mismatch between the validity of the spectra generated by traditional data enhancement methods and the accuracy of the categories. The surface-enhanced Raman spectroscopy (SERS) combined with the WGANGP-ResNet model established by this method for pathogenic bacteria Raman spectra classification reduces the need for a large amount of training data, facilitates rapid learning and analysis of Raman spectra with a low signal-to-noise-ratio, and reduces the spectra acquisition time to 1/10. It has important research significance and application value in pathogenic bacteria's rapid and culture-free clinical identification.
孟星志，刘亚秋，刘丽娜. 拉曼光谱结合WGANGP-ResNet算法鉴别病原菌种类[J]. 光谱学与光谱分析, 2024, 44(02): 542-547.
MENG Xing-zhi, LIU Ya-qiu, LIU Li-na. Raman Spectroscopy Combined With the WGANGP-ResNet Algorithm to Identify Pathogenic Species. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 542-547.
[1] Redd T K, Prajna N V, Srinivasan M, et al. Ophthalmology, 2022, 129(2): 227.
[2] Ivleva N P, Kubryk P, Niessner R. Anal. Bioanal. Chem., 2017, 409(18): 4353.
[3] Wang K, Li Z, Li J, et al. Trends in Food Science & Technology, 2021, 118: 490.
[4] Downes A. Applied Spectroscopy Reviews, 2019, 54(5): 445.
[5] Nicolson F, Jamieson L E, Mabbott S, et al. Journal of Raman Spectroscopy, 2017, 48(12): 1828.
[6] Lorenz B, Wichmann C, Stöckel S, et al. Trends in Microbiology, 2017, 25(5): 413.
[7] Ralbovsky N M, Lednev I K. Chemical Society Reviews, 2020, 49(20): 7428.
[8] Sheremetyeva N, Lamparski M, Daniels C, et al. Carbon, 2020, 169: 455.
[9] Ho C S, Jean N, Hogan C A, et al. Nat. Commun., 2019, 10(1): 4927.
[10] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Advances in Neural Information Processing Systems, 2014, 27: 2672.
[11] Yu S, Li H, Li X, et al. Science of The Total Environment, 2020, 726: 138477.
[12] LI Ling-qiao, LI Yan-hui, YIN Lin-lin, et al（李灵巧, 李彦晖, 殷琳琳, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2021, 41(2): 400.
[13] Gulrajani I, Ahmed F, Arjovsky M, et al. Advances in Neural Information Processing Systems 30 (NIPS 2017).
[14] Chakeri A, Sheikholeslam F. IEEE Transactions on Fuzzy Systems, 2013, 21(1): 171.
