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

Open access

Abstract

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.

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Ecological Chemistry and Engineering S

The Journal of Society of Ecological Chemistry and Engineering

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IMPACT FACTOR 2017: 0.7
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CiteScore 2017: 0.79

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