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J Sep Sci

Title:		A deep learning-based simulator for comprehensive two-dimensional GC applications
Author(s):		Minho LAC; Cardeal ZL; Menezes HC;
Address:		"Departamento de Quimica, ICEx, Universidade Federal de Minas Gerais, Avenida Antonio Carlos, Belo Horizonte, Minas Gerais, Brazil"
Journal Title:		J Sep Sci
Year:		2023
Volume:		20230731
Issue:
Page Number:		e2300187 -
DOI:		10.1002/jssc.202300187
ISSN/ISBN:		1615-9314 (Electronic) 1615-9306 (Linking)
Abstract:		"Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R(2) = 0.9202) and 0.014 (relative average deviation = 1.67%, R(2) = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users"
Keywords:		artificial intelligence collaboratory science data science machine learning public database;
Notes:		"PublisherMinho, Lucas Almir Cavalcante Cardeal, Zenilda de Lourdes Menezes, Helvecio Costa eng Companhia Energetica de Minas Gerais (CEMIG)/ Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)/ Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)/ Germany 2023/08/01 J Sep Sci. 2023 Jul 31:e2300187. doi: 10.1002/jssc.202300187"