Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy
文献类型：期刊论文
| 作者 | Weng, Shizhuang1; Yuan, Hecai1; Zhang, Xueyan1; Li, Pan2; Zheng, Ling1; Zhao, Jinling1; Huang, Linsheng1 |
| 刊名 | ANALYST
 |
| 出版日期 | 2020-07-21 |
| 卷号 | 145 |
| ISSN号 | 0003-2654 |
| DOI | 10.1039/d0an00492h |
| 通讯作者 | Weng, Shizhuang(weng_1989@126.com) ; Zheng, Ling() |
| 英文摘要 | Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in the matrix form, and the analysis was accomplished with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS. |
| WOS关键词 | BASE-LINE ; REGRESSION ; SCATTERING ; SPECTRA |
| 资助项目 | National Natural Science Foundation of China[3170123] ; National Natural Science Foundation of China[31971789] ; National Key Research and Development Program[2016YFD0800904] ; Open Foundation of Laboratory of Quality and Safety Risk Assessment on Agricultural Products (Beijing), Ministry of Agriculture[KFRA201802] |
| WOS研究方向 | Chemistry |
| 语种 | 英语 |
| WOS记录号 | WOS:000548664800009 |
| 出版者 | ROYAL SOC CHEMISTRY |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program ; Open Foundation of Laboratory of Quality and Safety Risk Assessment on Agricultural Products (Beijing), Ministry of Agriculture |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/102957]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Weng, Shizhuang; Zheng, Ling |
| 作者单位 | 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Ctr Med Phys & Technol, Hefei 230021, Peoples R China |
| 推荐引用方式 GB/T 7714 | Weng, Shizhuang,Yuan, Hecai,Zhang, Xueyan,et al. Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy[J]. ANALYST,2020,145. |
| APA | Weng, Shizhuang.,Yuan, Hecai.,Zhang, Xueyan.,Li, Pan.,Zheng, Ling.,...&Huang, Linsheng.(2020).Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy.ANALYST,145. |
| MLA | Weng, Shizhuang,et al."Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy".ANALYST 145(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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