射线吸收光谱学与深度学习相结合,用于自动和快速非法药物检测
Zheng Fang1, Xiefeng Zhan1, Bichao Ye1
1School of Aerospace Engineering, Xiamen University, Xiamen, China.
射线吸收光谱 (XAS) 与深度学习相结合,为快速,非破坏性药物识别提供了一种新,准确的方法. 一个改进的变压器模型实现了96.73%的准确性,超过了其他深度学习方法.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 射线吸收光谱 (XAS) 是一种利用差异吸收系数进行材料识别的物质分析技术.
- 深度学习与光谱技术的整合增强了自动化材料检测和识别精度.
- 目前的药物识别方法缺乏速度和非破坏性能力.
研究的目的:
- 开发一种用于快速,非破坏性检测禁药的新方法.
- 使用X射线吸收光谱 (XAS) 与常见的X射线管源和光子计数探测器用于药物识别.
- 使用先进的机器学习模型实现自动,快速和准确的药物检测.
主要方法:
- 使用CdTe探测器和标准X射线源收集的光谱数据.
- 将实验数据分为训练和测试集,用于模型评估.
- 使用改进的变压器编码器模型进行药物分类,将其性能与LSTM和ResU-net模型进行比较.
主要成果:
- 改进的变压器模型的训练时间为1.4小时,准确度为96.73%.
- 变压器模型的性能明显优于LSTM (2.6小时,准确率65%) 和ResU-net (1.5小时,准确率92.7%).
- 50种异构体或类似组成的药物物质被用于实验验证.
结论:
- 变压器模型中的注意力机制提高了光谱材料识别的准确性.
- XAS与深度学习相结合,为药物识别提供了高效和准确的解决方案.
- 这种方法显示出临床药物测试和执法应用的巨大潜力.
更多相关视频
10:17High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
Published on: April 23, 2019
11:14Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry
Published on: October 2, 2016
相关概念视频
X-ray Diffraction of Biological Samples
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are scattered by the electron clouds around the sample atoms. The X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
Atomic Absorption Spectroscopy: Lab
Solutions containing organic solvents, such as low-molecular-mass alcohols, esters, or ketones, enhance absorbances by increasing...
Applications of IR Spectroscopy: Overview
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview
The ATR process begins by directing a beam...
High-Performance Liquid Chromatography: Types of Detectors
Atomic Absorption Spectroscopy: Overview
When irradiated by EMR of a particular wavelength, these...
