用人工神经网络和机器学习算法开发一个用于拉曼光谱分析的集成工具箱
Xiangtao Kong1, Jie Xu1, Guodi Fan2
1Institute of Photonics and Photon-Technology, Northwest University, Xi'an 710069, China.
Molecules (Basel, Switzerland)
|February 27, 2026
概括
一个新的人工智能辅助拉曼光谱分析工具箱 (AI-Raman) 使用机器学习来分析体内拉曼光谱数据. 这种工具从指甲折光谱测量中准确预测葡萄糖度,有助于生物医学应用.
科学领域:
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
- 计算生物学 计算生物学
背景情况:
- 拉曼光谱为体内生物医学研究提供了丰富的化学特异性.
- 提取定量分子信息需要强大的模型,将光谱特征与成分联系起来.
- 开发先进的分析工具对于将拉曼光谱转化为临床实践至关重要.
研究的目的:
- 开发一个集成的软件工具箱,用于处理和分析拉曼光谱数据.
- 实施各种机器学习和人工神经网络算法进行定量分析.
- 评估用于体内生物医学测量的开发工具箱的性能.
主要方法:
- 开发了AI辅助拉曼光谱分析工具箱 (AI-Raman) V 1.0使用MATLAB R2024a.
- 集成的经典机器学习算法 (部分最小平方回归,支向量的回归) 和人工神经网络 (反向传播,卷积神经网络).
- 利用来自不同受试者的指甲折光谱数据集来评估软件的可行性和预测准确性.
主要成果:
- 人工智能-拉曼工具箱成功处理了拉曼光谱并进行了回归分析.
- 该软件展示了从体内指甲折拉曼光谱测量预测葡萄糖度的能力.
- 用户友好的图形界面允许自定义和优化分析模型.
结论:
- 人工智能-拉曼工具箱提供了一个强大的平台,用于对体内拉曼光谱数据进行定量分析.
- 开发的软件有助于准确预测葡萄糖度,显示生物医学应用的巨大潜力.
- 人工智能拉曼是推进基于拉曼的技术的宝贵工具,特别是在生物医学诊断和监测领域.
相关概念视频
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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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