Chemometrics is a science of extracting data from chemical processes and analyzing it utilizing basic mathematical and statistical methods [1, 2].
Role of qualitative chemical analysis increased significantly. This is due to the growing need for mass analysis of complex mixtures in such areas as analysis of environmental objects, verification of the authenticity of medical and biological drugs, food products, food raw materials, detection of toxicants, drugs, explosive substances. Concept of content qualitative chemical analysis has undergone significant changes. Today it is interpreted as a procedure for classifying objects based on their features. Qualitative chemical analysis solves the tasks of detection (establishing the presence of a certain analyte in the sample), identification (discrimination) (authentication of the analyte with a known individual substance or group of substances, assigning the sample to one of the pre-established classes) and clustering (identification of sets of samples with similar characteristics) of different objects under examination. The result of solving all these tasks is the classification of objects of analysis: during detection − division of samples into groups that contain the analyte in a concentration that exceeds the threshold and do not contain it; in case of identification − a conclusion about the identity of the studied sample and the standard or about the sample belonging to a certain class of objects based on their properties (supervised classification); during clustering − dividing the array of analyzed samples into groups of objects with similar characteristics [3-7].
Classification procedures should work satisfactorily at analyzing of compounds similar in structure and properties (at overlapping classes), as well as in the case of poorly described properties and presence the gaps in data arrays. To ensure high reliability of analyte classification, such arrays should be processed using effective methods of data analysis, in particular, chemometric ones.
Chemometrics has rapidly developed and as the result the arsenal of experimental data processing algorithms available to chemists is filled up. Algorithms of principal component analysis, discriminant analysis, fuzzy linear discriminant analysis, soft independent modeling of class analogy, support vector machines, classification and regression trees, projection on latent structures and different artificial neural networks are widely applied in chemistry for approximation and interpolation, recognition and classification, data compression, prediction and identification (pharmaceutical and medical application, food analysis, identification of environmental objects) [7-14].
There is an urgent need to identify among the chemometric algorithms the most effective for solving specific tasks of chemical experiment data processing, the development of new and such modernization of existing algorithms that will allow to reduce the requirements for the size and accuracy of the initial experimental data [3].
References
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2. Worsfold, P., Townshend, A., Poole, C. F., & Miró, M. (2019). Encyclopedia of analytical science. Elsevier.
3. Холін, Ю. В., Пушкарьова, Я. М., Пантелеймонов, А. В., & Некос, А. Н. (2017). Хемометричні методи в розв'язанні задач якісного хімічного аналізу та класифікації фізико-хімічних даних.
4. Vlasov, Y., Legin, A., Rudnitskaya, A., Di Natale, C., & D'amico, A. (2005). Nonspecific sensor arrays (“electronic tongue”) for chemical analysis of liquids (IUPAC Technical Report). Pure and Applied Chemistry, 77(11), 1965-1983.
5. Hardcastle, W. A. (2009). Qualitative analysis: a guide to best practice. Royal Society of Chemistry.
6. Pushkarova, Y. N., Sledzevskaya, A. B., Panteleimonov, A. V., Titova, N. P., Yurchenko, O. I., Ivanov, V. V., & Kholin, Y. V. (2013). Identification of water samples from different springs and rivers of Kharkiv: Comparison of methods for multivariate data analysis. Moscow University Chemistry Bulletin, 68(1), 60-66.
7. Краснянчин, Я. Н., Пантелеймонов, А. В., & Холин, Ю. В. (2010). Хемометрические методы в контроле подлинности продуктов питания и пищевого сырья. Методы и объекты химического анализа, 5(3), 118-147.
8. Bystrzanowska, M., & Tobiszewski, M. (2020). Chemometrics for selection, prediction, and classification of sustainable solutions for green chemistry − A review. Symmetry, 12(12), 2055.
9. Sauzier, G., van Bronswijk, W., & Lewis, S. W. (2021). Chemometrics in forensic science: approaches and applications. Analyst, 146(8), 2415-2448.
10. Brereton, R. G., Jansen, J., Lopes, J., Marini, F., Pomerantsev, A., Rodionova, O., ... & Tauler, R. (2017). Chemometrics in analytical chemistry − part I: history, experimental design and data analysis tools. Analytical and Bioanalytical Chemistry, 409(25), 5891-5899.
11. Brereton, R. G., Jansen, J., Lopes, J., Marini, F., Pomerantsev, A., Rodionova, O., ... & Tauler, R. (2018). Chemometrics in analytical chemistry − part II: modeling, validation, and applications. Analytical and bioanalytical chemistry, 410(26), 6691-6704.
12. Gummadi, S., & Chandaka, P. K. (2019). Chemometrics approach to drug analysis – An overview. Am. J. Pharm. Tech. Res, 9, 1-13.
13. Pushkarova, Y., Panchenko, V., & Kholin, Y. (2021, July). Application an Artificial Neural Network for Prediction of Substances Solubility. In IEEE EUROCON 2021-19th International Conference on Smart Technologies (pp. 82-87). IEEE.
14. Pushkarova, Y. N., Sledzevskaya, A. B., Semybratova, P. V., Garbuz, A. G., Nekos, A. N., & Kholin, Y. V. (2012). Identification of geographical origin of vegetables and fruits with the use of chemometric and statistical methods. Methods and objects of chemical analysis, 7(4), 184-191.
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