基于拉曼光谱学结合深度学习的提利亚宁定量分析研究
Wen Jiang1, Wei Liu2, Xiaotong Xin2
1The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
PloS one
|June 18, 2025
概括
这项研究引入了一种使用拉曼光谱的新型深度学习模型,用于对具有抗癌性质的化合物Tilianin的非破坏性定量分析. 开发的模型准确地预测了Tilianin度,改善了药物质量控制和临床应用.
科学领域:
- 药品分析 药品分析
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 提利亚宁具有显著的抗氧化,抗炎和抗癌活性,在治疗肝,肺和胃癌方面表现有前途.
- 精确的量化分析Tilianin对于药物质量控制,临床指导和疾病治疗至关重要,但目前的方法,如HPLC和MS往往是复杂的,破坏性的,需要专门的专业知识.
- 非破坏性方法,特别是那些结合拉曼光谱和深度学习的方法,为提利亚宁量化提供了一个有希望的替代方案.
研究的目的:
- 开发和验证一种新的深度学习模型,用于精确且非破坏性地对提利亚宁度进行定量分析.
- 对传统的机器学习和深度学习方法对拟议模型的性能进行评估.
- 建立一种更有效,更易于使用的提利亚宁量化方法.
主要方法:
- 制备六种不同度的提利亚宁-甲醇溶液.
- 使用拉曼光谱学收集120个光谱样本.
- 开发具有自我注意量化网 (RSAQN) 的拉曼光谱,其中包含用于特征提取的残余自我注意机制.
- 与随机森林,K-最近邻居,人工神经网络,卷积神经网络和变异自编码模型进行比较分析.
主要成果:
- 拟议的RSAQN模型在定量分析中表现出卓越的性能.
- 该模型实现了0.9144.4的高确定系数 (R2).
- 与基准模型相比,RSAQN模型的预测误差最小.
结论:
- 基于拉曼光谱和深度学习开发的残留自我注意力机制模型为提利素定量分析提供了一种有效和非破坏性的方法.
- 这种方法在样本处理和分析要求方面比传统方法具有优势.
- RSAQN模型显示了提高含有提利亚宁的药品质量控制和帮助临床应用的巨大潜力.
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