Application of Neural Networks in Diagnostics of Chemical Compounds Based on their Infrared Spectra

Open access


The paper presents possibilities of using the so-called „finger-print“ identification method and artificial neural network (ANN) for diagnosis of chemical compounds. The construction of a tool specifically developed for this purpose and the ANN, as well as the required conditions for its proper functioning were described. The identification of chemical compounds was tested in two different ways for proving correctness of the assumptions. First of all, initial studies were carried out with the objective to verify the proper functioning of the developed procedure for IR spectrum interpretation. The second research stage was to find out how the properties of artificial neural networks will satisfy identification or differentiation in case of spectra with very similar structures or for mixtures consisting of several chemical compounds. Interpretation of infrared spectra of mono-constituent substances was successfully performed for both - the training and test data. Interpretation process of infrared spectra of bi-component substances, based on the example of structurally related compounds obstructing identification process, should also be described as positive. The model was able to interpret spectra of mixtures, which were previously registered into the database. Unfortunately, the program is not always able to determine which chemical substances reflect their presence in the infrared spectrum of ternary mixtures. During the research tests, it was also noted that the more complex the structure of a substance being present in the mixture was, the more difficult the interpretation of the spectra to be carry out properly by the program was. On the other hand, positive results were obtained for mixtures of compounds with not so complex structure. It must be emphasized that the results so far are promising and more attention should be paid to them in further studies.

[1] Anilkumar GK. A subjective job scheduler based on a backpropagation neural network. Human-centric Computing Inform Sci. 2013;3:3-17. DOI: 10.1186/2192-1962-3-17.

[2] Babak MA, Sharafat AR, Setarehdan SK. An adaptive backpropagation neural network for arrhythmia classification using R-R interval signal. Neural Network World. 2012;6:535-548. DOI: 10.14311/NNW.2012.22.033.

[3] Balara D, Timko J, Žilková J. Application of neural network model for parameters identification of non-linear dynamic system. Neural Network World. 2013;2:103-116. DOI: 10.14311/NNW.2013.23.008.

[4] Klawun C, Wilkins CL. Neural network assisted rapid screening of large infrared spectral databases. Anal Chem. 1995;67(2):374-378. DOI: 10.1021/ac00098a023.

[5] Jalali-Heravi M. Neural networks in analytical chemistry. Methods Molecular Biol. 2008;458:78-118. DOI: 10.1007/978-1-60327-101-1_6.

[6] Polfer NC, Paizs B, Snoek LC, Compagnon I, Suhai S, Meijer G, et al. Infrared fingerprint spectroscopy and theoretical studies of potassium ion tagged amino acids and peptides in the gas chase. J Amer Chem Soc. 2005;127(23):8571-8579. DOI: 10.1021/ja050858u.

[7] McCarty GW, Reevesab JB, Reevesab VB, Follettc RF, Kimbled JM. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Amer J. 2002;66:640-646. DOI: 10.2136/sssaj2002.6400.

[8] Colthup NB, Wiberley SE, Daly LH. Introduction to Infrared and Raman Spectroscopy. New York and London: Academic Press; 1990.

[9] Silverstein RM, Webster FX, Kiemle DJ. Spectrometric Identification of Organic Compounds. New York: John Wiley Sons; 2014.

[10] Naumann D. A novel procedure for strain classification of fungal mycelium by cluster and artificial neural network analysis of Fourier transform infrared (FTIR) spectra. Analyst. 2009;134(6):1215-1223. DOI: 10.1039/b821286d.

[11] Coates J. Interpretation of Infrared Spectra, A Practical Approach. New York: John Wiley Sons; 2000. DOI: 10.1002/9780470027318.a5606.

[12] Xia M, Huang R, Sun Y, Semenza GL, Aldred SF, Witt KL, et al. Identification of chemical compounds that induce HIF-1alpha activity. Toxicol Sci. 2009;112(1):153-63. DOI: 10.1093/toxsci/kfp123.

[13] Srinivasan GV, Ranjith C, Vijayan KK. Identification of chemical compounds from the leaves of Leea indica. Acta Pharm. 2008;58(2):207-14. DOI: 10.2478/v10007-008-0002-7.

[14] Aguilera N, Becerra J, Villaseñor-Parada C, Lorenzo P, González L, Hernándeza V. Effects and identification of chemical compounds released from the invasive Acacia dealbata Link. Chem Ecol. 2015;31:479-493. DOI: 10.1080/02757540.2015.1050004.

[15] Tanaka M, Kuriyama S, Itoh G, Kohyama A, Iwabuchi Y, Shibata H, et al. Identification of anti-cancer chemical compounds using Xenopus embryos. Cancer Sci. 2016;107:803-811. DOI: 10.1111/cas.12940.

[16] Janczak A. Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach. Berlin: Springer Science Business Media; 2005.

[17] Tadeusiewicz R, Chaki R, Chaki N. Exploring Neural Networks with C#. Boca Raton: CRC Press; 2015.

[18] Heaton J. Programming Neural Networks with Encog 3 in C#. St. Louis: Heaton Research; 2011.

[19] Gudise VG, Venayagamoorthy GK. Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. Swarm Intelligence Symposium, 2003. DOI: 10.1109/SIS.2003.1202255.

[20] Mohammadi N, Mirabedini SJ. Comparison of particle swarm optimization and backpropagation algorithms for training feedforward neural network. J Mathemat Computer Sci. 2014;12:113-123.

Ecological Chemistry and Engineering S

The Journal of Society of Ecological Chemistry and Engineering

Journal Information

5-year IMPACT FACTOR: 0.815

CiteScore 2017: 0.79

SCImago Journal Rank (SJR) 2017: 0.227
Source Normalized Impact per Paper (SNIP) 2017: 0.535


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 596 596 55
PDF Downloads 495 495 46