基于SERS的前预测性化学分类学,用于未针对性地阐明epimeric cerebrosides的结构
Emily Xi Tan1, Shi Xuan Leong1, Wei An Liew1
1School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Nanyang, 637371, Singapore.
这项研究引入了一种机器学习框架,使用SERS以高精度识别和量化大脑类异构体. 该方法可以精确地阐明重要的生物标志物的结构和多重量化量化.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 机器学习 机器学习
背景情况:
- 在科学研究中,非向化学识别和异构体分化至关重要,但具有挑战性.
- 大脑蛋白,重要的脂质,存在于需要精确识别和定量化的epimers.
研究的目的:
- 开发一个基于表面增强拉曼光谱 (SERS) 的集成机器学习框架,用于对脑片的非有针对性的结构阐明.
- 为了获得精确的单个epimer和多重量化定量化cerebrosides.
主要方法:
- 使用4-mercaptophenylboronic酸进行选择性捕获epimers,生成epimer特定的SERS指纹.
- 建立了与结构特征相关的五个关键光谱特征 (epimer存在,糖类型,和度,糖系连接和碳链长度).
- 开发了一个可通用的机器学习框架,用于识别和量化.
主要成果:
- >90%的准确性在结构阐明11个表皮性脑细胞.
- 强大的单个epimer和多重量定量与<10%的错误.
- 在从10-4到10-10的度下检测和定量脑酸M.
结论:
- 开发的基于SERS的机器学习框架为非定位化学分析提供了强大的工具.
- 该框架允许精确识别,同位素分化和复杂的脂质结构的定量化,如大脑.
- 证明了含有生物标志物GlcCer24:1和GalCer24:1的二元混合物的成功多重量化.
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