Idioma:

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

Gaida, Meriem ; Stefanuto, Pierre-Hugues ; Focant, Jean-François

Journal of Chromatography A, 2023-11, Vol.1711, p.464467, Article 464467 [Periódico revisado por pares]

Elsevier B.V

Texto completo disponível

Citações Citado por

Enviar para

Título:
Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review
Autor: Gaida, Meriem ; Stefanuto, Pierre-Hugues ; Focant, Jean-François
Assuntos: Algorithms ; Analytical Chemistry ; Biochemistry ; Chemistry ; Chimie ; Chromatographic separations ; Chromatography (gas) ; Chromatography, Gas/methods ; Comprehensive two-dimensional gas chromatography ; Data processing ; General Medicine ; Machine Learning ; Machine learning algorithms ; Method development ; Model learning ; Modeling ; Organic Chemistry ; Physical, chemical, mathematical & earth Sciences ; Physique, chimie, mathématiques & sciences de la terre ; Support Vector Machine ; Theoretical modeling ; Thermodynamics ; Work-flows ; Workflow
É parte de: Journal of Chromatography A, 2023-11, Vol.1711, p.464467, Article 464467
Notas: scopus-id:2-s2.0-85174735162
Descrição: •Method development and data processing are essential for a robust GC × GC analysis.•Theoretical modeling improves the quality of chromatographic separation.•Machine learning-based workflows can enhance data processing in GC × GC–MS analyses.•Traditional statistical methods fall short with non-compliant datasets. In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square–Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.
Editor: Elsevier B.V
Idioma: Inglês

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

Gaida, Meriem ; Stefanuto, Pierre-Hugues ; Focant, Jean-François

Elsevier B.V

Buscando em bases de dados remotas. Favor aguardar.