Artificial Neural Networks: An Innovative Approach Used for Elucidation of Ionization Processes in Supercritical Fluid Chromatography-Mass Spectrometry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11160%2F25%3A10505649" target="_blank" >RIV/00216208:11160/25:10505649 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KP6Q.KRWIa" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KP6Q.KRWIa</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.analchem.5c00152" target="_blank" >10.1021/acs.analchem.5c00152</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neural Networks: An Innovative Approach Used for Elucidation of Ionization Processes in Supercritical Fluid Chromatography-Mass Spectrometry

Popis výsledku v původním jazyce

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 NH3 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.

Název v anglickém jazyce

Artificial Neural Networks: An Innovative Approach Used for Elucidation of Ionization Processes in Supercritical Fluid Chromatography-Mass Spectrometry

Popis výsledku anglicky

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 NH3 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30104 - Pharmacology and pharmacy

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Analytical Chemistry

ISSN

0003-2700

e-ISSN

1520-6882

Svazek periodika

97

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

10252-10263

Kód UT WoS článku

001485356300001

EID výsledku v databázi Scopus

2-s2.0-105004759873