Pregled bibliografske jedinice broj: 775738
COMPUTATIONALLY EFFICINET SEPARATION OF LARGE NUMBER OF ANALYTES FROM SMALL NUMBER OF MIXTURE MASS SPECTRA
COMPUTATIONALLY EFFICINET SEPARATION OF LARGE NUMBER OF ANALYTES FROM SMALL NUMBER OF MIXTURE MASS SPECTRA // Euroanalysis XVIII Book of Abstracts
Bordeaux, Francuska, 2015. str. 303-303 (poster, nije recenziran, sažetak, znanstveni)
CROSBI ID: 775738 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
COMPUTATIONALLY EFFICINET SEPARATION OF LARGE NUMBER OF ANALYTES FROM SMALL NUMBER OF MIXTURE MASS SPECTRA
Autori
Jerić, Ivanka ; Brkljačić, Lidija ; Kopriva, Ivica
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Euroanalysis XVIII Book of Abstracts
/ - , 2015, 303-303
Skup
EuroAnalysis 2015: 18th European Conference on Analytical Chemistry
Mjesto i datum
Bordeaux, Francuska, 06.09.2015. - 10.09.2015
Vrsta sudjelovanja
Poster
Vrsta recenzije
Nije recenziran
Ključne riječi
čiste komponente; spektrometrija mase; slijepo razdvajanje signala
(pure components; mass spectrometry; nonlinear blind source separation)
Sažetak
Contemporary metabolic profiling of biological samples aims to extract pure components from possibly nonlinear multicomponent mixtures mass spectra. Due to large number of components present in the mixtures spectra, related separation problem is hard. Herein, we further elaborate reproducible kernel Hilbert space method for nonlinear underdetermined blind source separation (uBSS) of components from mixtures mass spectra. In particular, we compare accuracy of approximate, but computationally more efficient method with the original one, developed recently on two problems related to: extraction of 25 components form 9 mixtures mass spectra, and extraction of 19 components from 12 mixtures mass spectra. Mixtures mass spectra (m1-mn) were obtained from chemical reaction of peptide formation at different time points (t0-tm-1). By using approximate version of pure components extraction method it is possible to drastically reduce computation time while still retaining decent separation quality. This result is of practical relevance, because approximate method combined with high-speed implementation on graphical processing units enables nearly real time execution. Here presented approach is of wide application, such as non-targeted metabolite profiling or dynamic libraries search.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Kemija, Računarstvo
