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FINDING EXACT NUMBER OF PEAKS IN BROADBAND UV-VIS SPECTRA USING CURVE FITTING METHOD BASED ON EVOLUTIONARY COMPUTING

Yıl 2020, Cilt: 7 Sayı: 1, 117 - 124, 15.02.2020
https://doi.org/10.18596/jotcsa.583632

Öz



High performance calculations are
needed in order to resolve analytic signals of the day. But it requires very
long periods of time to perform these calculations with single processor
systems. In order to reduce these calculation times, there is a need to turn to
parallel programming algorithms that share more than one processor. Recently,
solving complex problems with genetic algorithms has been widely used in
computational sciences. In this work, we show a new method of curve fitting via
genetic algorithm based on Gaussian functions, for deconvolution the
overlapping peaks and find the exact number of peaks in UV-VIS absorption
spectroscopy. UV-VIS spectra are different than other instrumental analysis
data. The resolution of UV-VIS spectra is a complicated because of that the
absorption bands are strongly overlapped. Useful information about molecular
structure and environment can often be obtained by resolving these peaks
properly. The algorithm was parallelized with the island model in which each
processor computes a different population. This method has been used for resolving
of the UV-VIS overlapping spectrum. The method particular algorithm is robust
by bad resolution or noise. The results show that it is satisfactory and
clearly show the effectiveness of the proposed method.




Destekleyen Kurum

Inonu University Scientific Research Project Department

Proje Numarası

FBA-2019- 1726

Kaynakça

  • L. A. Rastrigin, The convergence of the random search method in the extremal control of a many-parameter system, Automat. Remote Control 24 (11), 1963, pp. 1337–1342
  • J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, University of Michigan Press, 1975.
  • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  • C. B. Lucasius, G. Kateman, Application of genetic algorithms in chemometrics, in: Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989, pp. 170–176
  • M. Karakaplan, Fitting lorentzian peaks with evolutionary genetic algorithm based on stochastic search procedure, Analytica Chimica Acta 587 (2), 2007 pp. 235–239
  • W. D. Whitley, S. B. Rana, R. B. Heckendorn, Island model genetic algorithms and linearly separable problems, in: Selected Papers from AISB Workshop on Evolutionary Computing, Springer-Verlag, London, UK, UK, , 1997, pp. 109–125
  • R. Wehrens, L. M. Buydens, Evolutionary optimisation: a tutorial, TrAC Trends in Analytical Chemistry 17 (4), 1998, pp. 193 – 203
  • D. M. Deaven, K. M. Ho, Molecular geometry optimization with a genetic algorithm, Physical Review Letters 75 (2), 1995, pp. 288–291
  • J. Zhao, R.-H. Xie, Genetic algorithms for the geometry optimization of atomic and molecular clusters, Journal of Computational and Theoretical Nanoscience 1 (2), 2004, pp. 117–131
  • M. A. Addicoat, A. J. Page, Z. E. Brain, L. Flack, K. Morokuma, S. Irle, Optimization of a genetic algorithm for the functionalization of fullerenes, Journal of Chemical Theory and Computation 8 (5), 2012, pp. 1841–1851
  • R. P. F. Kanters, K. J. Donald, cluster: Searching for unique low energy minima of structures using a novel implementation of a genetic algorithm, Journal of Chemical Theory and Computation 10 (12), 2014, pp. 5729–5737
  • N. Alexandrova, A. I. Boldyrev, Search for the lin0/+1/-1 (n = 57) lowest-energy structures using the ab initio gradient embedded genetic algorithm (gega). elucidation of the chemical bonding in the lithium clusters, Journal of Chemical Theory and Computation 1 (4), 2005, pp. 566–580
  • M. Chen, D. A. Dixon, Tree growthhybrid genetic algorithm for predicting the structure of small (tio2)n, n = 213, nanoclusters, Journal of Chemical Theory and Computation 9 (7), 2013, pp. 3189–3200
  • K. Tang, K. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 13 (6), 1996 , pp. 22–37
  • E. Jones, P. Runkle, N. Dasgupta, L. Couchman, L. Carin, Genetic algorithm wavelet design for signal classification, IEEE Trans. Pattern Anal. Mach. Intell. 23 (8), 2001, pp. 890–895
  • M. Gulsen, A. E. Smith, D. M. Tate, A genetic algorithm approach to curve fitting, International Journal of Production Research 33 (7), 1995, pp. 1911–1923
  • P. Vankeerberghen, J. Smeyers-Verbeke, R. Leardi, C. L. Karr, D. L. Massart, Robust regression and outlier detection for non-linear models using genetic algorithms, Chemometrics and Intelligent Laboratory Systems 28 (1), 1995, pp. 73–87
  • H. Ahonen, P. A. de Souza Jnior, V. K. Garg, A genetic algorithm for fitting lorentzian line shapes in mssbauer spectra, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 124 (4), 1997, pp. 633–638
  • C. Lucasius, G. Kateman, Genetic algorithms for large-scale optimization in chemometrics: An application, TrAC Trends in Analytical Chemistry 10 (8), 1991, pp. 254 – 261
  • H. Muhlenbein, Evolution in time and space - the parallel genetic algorithm, in: Foundations of Genetic Algorithms, Morgan Kaufmann, 1991, pp. 316–337
  • H. Muhlenbein, M. Schomisch, J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (67), 1991, pp. 619–632
  • C. B. Louise, M. C. Tran, T. G. Obrig, Sensitization of human umbilical vein endothelial cells to shiga toxin: involvement of protein kinase c and NF-kappaB., Infection and Immunity 65 (8), 1997, pp. 3337–3344
  • MPICH | high-performance portable MPI. URL http://www.mpich.org/ (2019).
  • M. Karakaplan, F. M. Avcu, A parallel and non-parallel genetic algorithm for deconvolution of NMR spectra peaks, Chemometrics and Intelligent Laboratory Systems 125 (0), 2013, pp. 147 – 152
Yıl 2020, Cilt: 7 Sayı: 1, 117 - 124, 15.02.2020
https://doi.org/10.18596/jotcsa.583632

Öz

Proje Numarası

FBA-2019- 1726

Kaynakça

  • L. A. Rastrigin, The convergence of the random search method in the extremal control of a many-parameter system, Automat. Remote Control 24 (11), 1963, pp. 1337–1342
  • J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, University of Michigan Press, 1975.
  • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  • C. B. Lucasius, G. Kateman, Application of genetic algorithms in chemometrics, in: Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989, pp. 170–176
  • M. Karakaplan, Fitting lorentzian peaks with evolutionary genetic algorithm based on stochastic search procedure, Analytica Chimica Acta 587 (2), 2007 pp. 235–239
  • W. D. Whitley, S. B. Rana, R. B. Heckendorn, Island model genetic algorithms and linearly separable problems, in: Selected Papers from AISB Workshop on Evolutionary Computing, Springer-Verlag, London, UK, UK, , 1997, pp. 109–125
  • R. Wehrens, L. M. Buydens, Evolutionary optimisation: a tutorial, TrAC Trends in Analytical Chemistry 17 (4), 1998, pp. 193 – 203
  • D. M. Deaven, K. M. Ho, Molecular geometry optimization with a genetic algorithm, Physical Review Letters 75 (2), 1995, pp. 288–291
  • J. Zhao, R.-H. Xie, Genetic algorithms for the geometry optimization of atomic and molecular clusters, Journal of Computational and Theoretical Nanoscience 1 (2), 2004, pp. 117–131
  • M. A. Addicoat, A. J. Page, Z. E. Brain, L. Flack, K. Morokuma, S. Irle, Optimization of a genetic algorithm for the functionalization of fullerenes, Journal of Chemical Theory and Computation 8 (5), 2012, pp. 1841–1851
  • R. P. F. Kanters, K. J. Donald, cluster: Searching for unique low energy minima of structures using a novel implementation of a genetic algorithm, Journal of Chemical Theory and Computation 10 (12), 2014, pp. 5729–5737
  • N. Alexandrova, A. I. Boldyrev, Search for the lin0/+1/-1 (n = 57) lowest-energy structures using the ab initio gradient embedded genetic algorithm (gega). elucidation of the chemical bonding in the lithium clusters, Journal of Chemical Theory and Computation 1 (4), 2005, pp. 566–580
  • M. Chen, D. A. Dixon, Tree growthhybrid genetic algorithm for predicting the structure of small (tio2)n, n = 213, nanoclusters, Journal of Chemical Theory and Computation 9 (7), 2013, pp. 3189–3200
  • K. Tang, K. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 13 (6), 1996 , pp. 22–37
  • E. Jones, P. Runkle, N. Dasgupta, L. Couchman, L. Carin, Genetic algorithm wavelet design for signal classification, IEEE Trans. Pattern Anal. Mach. Intell. 23 (8), 2001, pp. 890–895
  • M. Gulsen, A. E. Smith, D. M. Tate, A genetic algorithm approach to curve fitting, International Journal of Production Research 33 (7), 1995, pp. 1911–1923
  • P. Vankeerberghen, J. Smeyers-Verbeke, R. Leardi, C. L. Karr, D. L. Massart, Robust regression and outlier detection for non-linear models using genetic algorithms, Chemometrics and Intelligent Laboratory Systems 28 (1), 1995, pp. 73–87
  • H. Ahonen, P. A. de Souza Jnior, V. K. Garg, A genetic algorithm for fitting lorentzian line shapes in mssbauer spectra, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 124 (4), 1997, pp. 633–638
  • C. Lucasius, G. Kateman, Genetic algorithms for large-scale optimization in chemometrics: An application, TrAC Trends in Analytical Chemistry 10 (8), 1991, pp. 254 – 261
  • H. Muhlenbein, Evolution in time and space - the parallel genetic algorithm, in: Foundations of Genetic Algorithms, Morgan Kaufmann, 1991, pp. 316–337
  • H. Muhlenbein, M. Schomisch, J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (67), 1991, pp. 619–632
  • C. B. Louise, M. C. Tran, T. G. Obrig, Sensitization of human umbilical vein endothelial cells to shiga toxin: involvement of protein kinase c and NF-kappaB., Infection and Immunity 65 (8), 1997, pp. 3337–3344
  • MPICH | high-performance portable MPI. URL http://www.mpich.org/ (2019).
  • M. Karakaplan, F. M. Avcu, A parallel and non-parallel genetic algorithm for deconvolution of NMR spectra peaks, Chemometrics and Intelligent Laboratory Systems 125 (0), 2013, pp. 147 – 152
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Analitik Kimya
Bölüm Makaleler
Yazarlar

Fatih Mehmet Avcu 0000-0002-1973-7745

Mustafa Karakaplan 0000-0002-9664-4112

Proje Numarası FBA-2019- 1726
Yayımlanma Tarihi 15 Şubat 2020
Gönderilme Tarihi 28 Haziran 2019
Kabul Tarihi 22 Kasım 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 1

Kaynak Göster

Vancouver Avcu FM, Karakaplan M. FINDING EXACT NUMBER OF PEAKS IN BROADBAND UV-VIS SPECTRA USING CURVE FITTING METHOD BASED ON EVOLUTIONARY COMPUTING. JOTCSA. 2020;7(1):117-24.