人工神经网络:在超临界流体染色学-质谱学中用于阐明电离过程的创新方法
Kateřina Plachká1, Veronika Pilařová1, Tat Ána Gazárková1
1Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 03 Hradec Králové, Czechia.
Analytical chemistry
|May 10, 2025
概括
人工神经网络在超临界流体染色学 (SFC-MS) 中预测质谱反应. 这有助于通过了解分子特性和溶剂如何影响电离效率来优化检测.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 超临界流体染色学-质谱学 (SFC-MS) 对于检测各种分析物至关重要.
- 由于复杂的分析剂-溶剂相互作用,预测MS反应具有挑战性.
研究的目的:
- 使用人工神经网络 (ANN) 开发SFC-MS反应的预测模型.
- 在各种化溶剂中探索分子描述符和MS反应之间的关系.
- 为了提高对SFC-MS的电离效率的理解.
主要方法:
- 在标准化SFC条件下对化合物的226个分子描述符进行了评估.
- 测试了24种不同的化溶剂成分,包括含添加剂的酒精和甲醇.
- 采用人工神经网络 (ANN) 来建模分析物描述物和MS反应.
- 利用主要成分分析 (PCA) 来分析描述器对电离的贡献.
主要成果:
- 在SFC-MS中,ANN模型有效地将分子描述符与MS反应相关联.
- 对于电子喷雾电离 (ESI) 和大气压化学电离 (APCI) 观察到不同的电离机制.
- 分子描述器在积极模式下区分ESI和APCI之间的电离效率,在负模式下差异较小.
结论:
- 使用ANN的预测模型可以进一步了解SFC-MS中的电离效率.
- 这些发现为优化SFC-MS工作流程提供了一个框架,通过考虑分子特性和溶剂效应.
- 该研究强调了化溶剂成分,特别是氨,对电离歧视的影响.
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