Deconvolution of Gaussian peaks with mixed real and discrete-integer optimization based on evolutionary computing

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dc.authoridAVCU, Fatih Mehmet/0000-0002-1973-7745
dc.authoridKarakaplan, Mustafa/0000-0002-9664-4112
dc.authorwosidAVCU, Fatih Mehmet/ABG-8390-2020
dc.authorwosidKarakaplan, Mustafa/H-3708-2018
dc.contributor.authorKarakaplan, Mustafa
dc.contributor.authorAvcu, Fatih Mehmet
dc.date.accessioned2024-08-04T20:47:23Z
dc.date.available2024-08-04T20:47:23Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study describes an alternative method for deconvolution of overlapping characteristic Gauss peaks with the help of optimization of a mixed variable genetic algorithm. Continuous and discrete variables and nonlinear discrete variables in optimization problems cause solution complexity. The processing and analysis of complex analytical signals is important not only in analytical chemistry but also in other fields of science. As the amount of data increases and linearity decreases, high-performance computations are needed to solve analytical signals. It takes a long time to perform these calculations with traditional processor systems and algorithms. We have used NVIDIA graphical processing units (GPUs) to shorten the duration of these calculations. Solving such analytical signals with genetic algorithms is widely used in computational sciences. In this study, we present a new curve-fitting method using a genetic algorithm based on Gauss functions used to deconvolve overlapping peaks and find the exact peak number in absorption spectroscopy. The deconvolution of individual bands in the UV-VIS region is a complex task, because the absorption bands are broad and often strongly overlap. Useful information about the molecular structure and environment can only be obtained by appropriate and truthful separation of these peaks.en_US
dc.description.sponsorshipInonu University Scientific Research Project Department [FBA-2019-1726]en_US
dc.description.sponsorshipInonu University Scientific Research Project Department, Grant/Award Number: FBA-2019-1726en_US
dc.identifier.doi10.1002/cem.3229
dc.identifier.issn0886-9383
dc.identifier.issn1099-128X
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85085863242en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cem.3229
dc.identifier.urihttps://hdl.handle.net/11616/99327
dc.identifier.volume34en_US
dc.identifier.wosWOS:000515058900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of Chemometricsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectabsorption spectroscopyen_US
dc.subjectdata fittingen_US
dc.subjectgenetic algorithmen_US
dc.subjectGPU programmingen_US
dc.subjectpeak overlapen_US
dc.titleDeconvolution of Gaussian peaks with mixed real and discrete-integer optimization based on evolutionary computingen_US
dc.typeArticleen_US

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