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Wavelet transform and artificial neutral network for the quantitative resolution of ternary mixtures

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dc.contributor.author Taş, Ayşegül
dc.contributor.author Dinç, Erdal
dc.contributor.author Baleanu, Dumitru
dc.date.accessioned 2020-04-10T09:34:31Z
dc.date.available 2020-04-10T09:34:31Z
dc.date.issued 2006-06
dc.identifier.citation Dinç, Erdal; Baleanu,Dumitru, Taş, ayşegül, "Wavelet transform and artificial neutral network for the quantitative resolution of ternary mixtures", Revista De Chimie, Vol.57, No.6, pp.626-631, (2006). tr_TR
dc.identifier.issn 0034-7752
dc.identifier.uri http://hdl.handle.net/20.500.12416/3053
dc.description.abstract Two different approaches continuous wavelet transform (CWT) and artificial neural network (ANN) were successfully applied to the quantitative resolution of ternary mixtures of paracetamol (PAR), metamizol (MET) and caffeine (CA) having strongly overlapping spectra. First approach is based on the use of CWT signals of the ratio spectra of three active compounds in samples. Various CWT families were tested for the extraction of cardinal information and small noise condition and higher peaks of the original spectra. In this paper, three methods: Mexican hat function (MEX) (a = 70), reverse biorthogonal (RBIO3.5) (a = 100) and biorthogonal (BIOR2.4) (a = 90) were found suitable for determination of three active compounds and their signal analysis. ANN, as a multivariate numerical method, was used for the quantitative resolution of the same ternary mixtures. The performance of CWT and ANN approaches was validated by analyzing the synthetic mixtures of PAR, MET and CA compounds. In addition, the standard addition technique was also used for the same purpose. The experimental results provided by CWT technique were compared with each other and those obtained by ANN method and a coincidence was observed for all the obtained results. tr_TR
dc.language.iso eng tr_TR
dc.publisher Chiminform Data SA tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Continuous Wavelet Transforms tr_TR
dc.subject Artificial Neural Network tr_TR
dc.subject Ratio Spectra tr_TR
dc.subject Zero-Crossing Technique tr_TR
dc.subject Ternary Mixture tr_TR
dc.title Wavelet transform and artificial neutral network for the quantitative resolution of ternary mixtures tr_TR
dc.type article tr_TR
dc.relation.journal Revista De Chimie tr_TR
dc.contributor.authorID 56389 tr_TR
dc.contributor.authorID 29252 tr_TR
dc.identifier.volume 57 tr_TR
dc.identifier.issue 6 tr_TR
dc.identifier.startpage 626 tr_TR
dc.identifier.endpage 631 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü; İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü tr_TR


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