Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy
文献类型:期刊论文
作者 | Gao, Chi1,2,3; Zhao, Peng1,2,3; Fan, Qi2,3; Jing, Haonan1,2,3; Dang, Ruochen1,2,3; Sun, Weifeng1,2,3; Feng, Yutao3; Hu, Bingliang2,3; Wang, Quan2,3 |
刊名 | SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY |
出版日期 | 2023-12-05 |
卷号 | 302 |
ISSN号 | 1386-1425;1873-3557 |
关键词 | Raman spectroscopy Deep learning Baseline correction Spectroscopy denoising |
DOI | 10.1016/j.saa.2023.123086 |
产权排序 | 1 |
英文摘要 | Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:001058399900001 |
源URL | [http://ir.opt.ac.cn/handle/181661/96761] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Wang, Quan |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Key Lab Biomed Spect Xian, Xian 710119, Shaanxi, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710076, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Gao, Chi,Zhao, Peng,Fan, Qi,et al. Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2023,302. |
APA | Gao, Chi.,Zhao, Peng.,Fan, Qi.,Jing, Haonan.,Dang, Ruochen.,...&Wang, Quan.(2023).Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,302. |
MLA | Gao, Chi,et al."Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 302(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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