机器学习辅助的表面增强的Raman光谱用于快速确定谷二氧化还原比
Wilson A Garuba1, Brian A Barth2, Adam E Imel3
1Department of Chemistry, University of Tennessee, Knoxville, TN 37996, USA. bhavya.sharma@utk.edu.
The Analyst
|April 8, 2025
概括
我们开发了一种机器学习方法,与表面增强拉曼光谱学 (SERS) 结合起来,以准确检测谷氨 (GSH/GSSG) 比率. 这种方法克服了复杂的生物样本中的挑战,使得精确的氧化应激监测成为可能.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 谷氨 (GSH和GSSG) 对于细胞氧化还原平衡和氧化应激监测至关重要.
- 表面增强的拉曼光谱 (SERS) 提供了敏感的生物分子检测,但与类似分子的重叠信号作斗争.
- 精确量化GSH:GSSG比率对于了解与氧化应激相关的疾病状态至关重要.
研究的目的:
- 开发一种可靠的方法来准确量化水溶液中的谷氨氧化还原比 (GSH:GSSG).
- 将机器学习 (ML) 算法与SERS集成,以克服光谱重叠的挑战.
- 为快速和精确评估氧化应激标志物建立一个实用的协议.
主要方法:
- 利用表面增强的拉曼光谱法 (SERS) 来获得谷氨混合物的光谱.
- 应用机器学习算法,包括支持向量回归 (SVR),极端梯度提升 (XGBoost) 和多层感知器 (MLP).
- 经过训练和验证的ML模型使用各种GSH:GSSG比率的预处理SERS光谱.
主要成果:
- 多层感知器 (MLP) 模型在量化GSH:GSSG比率方面表现出卓越的性能.
- 在试验组中,MLP获得了0.966的高相关系数 (Q2),表明了卓越的准确性和稳定性.
- 集成的ML-SERS方法成功地解决了光谱重叠,使得精确的比率确定.
结论:
- 机器学习与SERS相结合,为准确的谷氨氧化还原比率确定提供了一个强大的工具.
- 这种方法为快速可靠地监测氧化应激提供了一个实用的解决方案.
- 开发的协议对生物化学分析和疾病诊断有重大影响.
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