Plachká Kateřina, Pilařová Veronika, Gazárková Tat Ána, Garrigues Jean-Christophe, Švec František, Nováková Lucie
Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 03 Hradec Králové, Czechia.
SOFTMAT (IMRCP) Laboratory, SMODD Team, CNRS, Toulouse III Paul Sabatier University, 31400 Toulouse, France.
Anal Chem. 2025 May 20;97(19):10252-10263. doi: 10.1021/acs.analchem.5c00152. Epub 2025 May 10.
Understanding and predicting mass spectrometry responses in supercritical fluid chromatography-mass spectrometry (SFC-MS) is critical for optimizing detection across diverse analytes and solvent compositions. We present a novel approach using artificial neural networks (ANN) to explore the complex relationships between molecular descriptors of analytes and MS responses in different makeup solvent compositions enabling SFC-MS coupling. 226 molecular descriptors were evaluated for compounds under standardized SFC conditions, with 24 makeup solvent compositions. These makeup solvents included pure alcohols and methanol with varying concentrations of volatile additives. Our results highlight distinct ionization processes for the two most commonly used soft ionization techniques: (i) electrospray ionization (ESI), primarily involving proton or cation transfer, and (ii) atmospheric pressure chemical ionization (APCI), associated with charged ion transfer. Principal component analysis of weights assigned to molecular descriptors reveals that, in positive detection mode, these descriptors effectively differentiate ionization efficiency between ESI and APCI. In contrast, this differentiation is less pronounced in negative mode, where the variance explained is more homogeneously distributed, with stronger discrimination observed when NH is used as an additive to the organic modifier. These findings provide critical insights into the influence of molecular descriptors and solvent composition on ionization efficiency, serving as a foundation for future investigations into SFC-MS optimization. This proof-of-concept underscores the feasibility of using predictive models to advance understanding of ionization efficiency and offers a valuable framework for refining SFC-MS workflows in analytical chemistry.
了解和预测超临界流体色谱-质谱联用（SFC-MS）中的质谱响应对于优化不同分析物和溶剂组成下的检测至关重要。我们提出了一种使用人工神经网络（ANN）的新方法，以探索分析物的分子描述符与不同补充溶剂组成下的质谱响应之间的复杂关系，从而实现SFC-MS联用。在标准化的SFC条件下，对24种补充溶剂组成的化合物评估了226个分子描述符。这些补充溶剂包括纯醇类以及含有不同浓度挥发性添加剂的甲醇。我们的结果突出了两种最常用的软电离技术的不同电离过程：（i）电喷雾电离（ESI），主要涉及质子或阳离子转移；（ii）大气压化学电离（APCI），与带电离子转移有关。对分配给分子描述符的权重进行主成分分析表明，在正检测模式下，这些描述符有效地区分了ESI和APCI之间的电离效率。相比之下，在负模式下这种区分不太明显，其中解释的方差分布更均匀，当NH用作有机改性剂的添加剂时观察到更强的区分。这些发现为分子描述符和溶剂组成对电离效率的影响提供了关键见解，为未来SFC-MS优化研究奠定了基础。这一概念验证强调了使用预测模型来增进对电离效率理解的可行性，并为完善分析化学中的SFC-MS工作流程提供了有价值的框架。
