基于深度学习的现场 SERS 分析及芬太尼类物质智能多重识别
On-site SERS analysis and intelligent multi-identification of fentanyl class substances by deep machine learning.
机构信息
Shanghai Key Lab of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai 200063, China; Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou 317000, Zhejiang, China.
College of Sciences, Nanjing Agricultural University, Nanjing 210095, China.
出版信息
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Jan 15;325:125090. doi: 10.1016/j.saa.2024.125090. Epub 2024 Sep 5.
As the types of fentanyl class substances continue to grow, a universal SERS sensor is essential for the application of discriminant detection of fentanyl substances. A new nanomaterial SERS sensor-Ag@Au NPs-paper was developed. The SERS sensitivity and stability of Ag@Au NPs-paper were investigated by using R6G molecule, and the results showed that Ag@Au NPs-paper has excellent performance. In combination with visual analysis and machine learning methods, Ag@Au NPs-paper has been successfully applied to the analysis of fentanyl class substances and the component identification of binary fentanyl mixtures, and thus it can be effectively used in food safety, environmental toxicants and other fields.
随着芬太尼类物质的种类不断增加，通用的 SERS 传感器对于芬太尼类物质的判别检测应用至关重要。一种新的纳米材料 SERS 传感器 - Ag@Au NPs-纸被开发出来。通过使用 R6G 分子研究了 Ag@Au NPs-纸的 SERS 灵敏度和稳定性，结果表明 Ag@Au NPs-纸具有优异的性能。结合视觉分析和机器学习方法，Ag@Au NPs-纸已成功应用于芬太尼类物质的分析和二元芬太尼混合物的成分鉴定，因此可有效应用于食品安全、环境毒物等领域。
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