SavedAdd your journal

Journals / Journal of the Turkish Chemical Society Section A: Chemistry / 2026 / Volume: 7 Issue: 1

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

Fatih Mehmet Avcu(INONU UNIVERSITY)0000-0002-1973-7745
Mustafa Karakaplan(INONU UNIVERSITY)0000-0002-9664-4112
Pages
—
Publication Date
2020-02-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that it is satisfactory and clearly show the effectiveness of the proposed method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:High performance calculations are needed in order to resolve analytic signals of the day.
📊
Key Findings & Data:But it requires very long periods of time to perform these calculations with single processor systems.
💡
Conclusion & Impact:The results show that it is satisfactory and clearly show the effectiveness of the proposed method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:UV-VIS spectroscopy · Data fitting · Genetic algorithm

Abstract

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.

🔖

Cite This Article

Avcu, F. M., Karakaplan, M. (2020). FINDING EXACT NUMBER OF PEAKS IN BROADBAND UV-VIS SPECTRA USING CURVE FITTING METHOD BASED ON EVOLUTIONARY COMPUTING. Journal of the Turkish Chemical Society Section A: Chemistry, Volume: 7 Issue: 1. https://doi.org/10.18596/jotcsa.583632

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (26)

  1. 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
  2. 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.
  3. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  4. 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
  5. M. Karakaplan, Fitting lorentzian peaks with evolutionary genetic algorithm based on stochastic search procedure, Analytica Chimica Acta 587 (2), 2007 pp. 235–239
  6. 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
  7. R. Wehrens, L. M. Buydens, Evolutionary optimisation: a tutorial, TrAC Trends in Analytical Chemistry 17 (4), 1998, pp. 193 – 203
  8. D. M. Deaven, K. M. Ho, Molecular geometry optimization with a genetic algorithm, Physical Review Letters 75 (2), 1995, pp. 288–291
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. K. Tang, K. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 13 (6), 1996, pp. 22–37
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. H. Muhlenbein, Evolution in time and space - the parallel genetic algorithm, in: Foundations of Genetic Algorithms, Morgan Kaufmann, 1991, pp. 316–337
  21. H. Muhlenbein, M. Schomisch, J. Born, The parallel genetic algorithm as function optimizer, Parallel Computing 17 (67), 1991, pp. 619–632
  22. 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
  23. MPICH | high-performance portable MPI. URL http://www.mpich.org/ (2019).
  24. 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
  25. 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
  26. K. Tang, K. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 13 (6), 1996, pp. 22–37