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Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index

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This study aims to compare linear and nonlinear prediction methods in predicting the Kovats retention index of 126 compounds extracted from the Lippia origanoides plant. The retention index of each compound has been predicted based on its molecular descriptors. There have been 189 molecular descriptors for each compound in this study, and the best descriptors have been selected using the Genetic Algorithm (GA). It has succeeded in selecting the best five descriptors used to build the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) model. MLR has obtained R2 of 0.959, 0.946, 0.955, and a Root Mean Square Error (RMSE) of 48.00, 50.84, 47.19 on training, validation, and testing, respectively. Meanwhile, ANN has obtained R2 of 0.963, 0.947, 0.962, and an RMSE of 45.45, 50.59, 43.20, respectively. Compared to MLR, there has been an increase in the ANN model's performance, with an increase in R2 of 0.004, 0.001, and 0.007 and a decrease in RMSE of 2.55, 0.25, and 3.99. Based on the prediction results obtained, it is known that in this case, the ANN method can provide better predictive results than MLR.
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Kovats Retention Index; Lippia Origanoides; Essential Oil; Molecular Descriptor; Genetic Algorithm; Multiple Linear Regression; Artificial Neural Network

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R. Idroes, Muslem, Mahmudi, Saiful, G.M. Idroes, R. Suhendra, Irvanizam, The effect of column and temperature variation on the determination of the dead time in gas chromatographic systems using indirect methods, Heliyon, Vol. 6(no. 2), pp. e03302–e03302, February 2020.

S. Utami Tunjung Pratiwi, E. Lagendijk, S. Weert, R. Idroes, T. Hertiani, and C. Hondel, Effect of Cinnamomum burmannii Nees ex Bl. and Massoia aromatica Becc. Essential Oils on Planktonic Growth and Biofilm formation of Pseudomonas aeruginosa and Staphylococcus aureus In Vitro, Int. J. Appl. Res. Nat. Prod., Vol. 8, pp. 1–13, March 2015.

N. Earlia, R. Suhendra, M. Amin, C. R. S. Prakoeswa, and R. Idroes, GC/MS Analysis of Fatty Acids on Pliek U Oil and Its Pharmacological Study by Molecular Docking to Filaggrin as a Drug Candidate in Atopic Dermatitis Treatment, Sci. World J., Vol. 2019, 2019.

N. Earlia, R. Rahmad, M. Amin, C. Prakoeswa, K. Khairan, and R. Idroes, The Potential Effect of Fatty Acids from Pliek U on Epidermal Fatty Acid Binding Protein: Chromatography and Bioinformatic Studies, Sains Malaysiana, Vol. 48(no. 5), pp. 1019–1024, May 2019.

E.C. Estevam, S. Griffin, M.J. Nasim, D. Zieliński, J. Aszyk, M. Osowicka, N. Dawidowska, R. Idroes, A. Bartoszek, C. Jacob, Inspired by Nature: The Use of Plant-derived Substrate/Enzyme Combinations to Generate Antimicrobial Activity in situ., Nat. Prod. Commun., 2015.

R. Idroes, Muslem, Mahmudi, Saiful, M. Paristiowati, G.M. Idroes, R. Suhendra, A. Maulana, T.R. Noviandy, Irvanizam, The influence of temperatures, polarity, modifier and pressure to retention index in supercritical fluid chromatography: A review, IOP Conf. Ser. Mater. Sci. Eng., 2021.

R. Idroes, I. Husna, Muslem, Mahmudi, A. Rusyana, Z. Helwani, G.M. Idroes, R. Suhendra, E. Yandri, S. Rahimah, N.R. Sasmita, Analysis of temperature and column variation in gas chromatography to dead time of inert gas and n-alkane homologous series using randomized block design, in IOP Conference Series: Earth and Environmental Science, Vol. 364 (no. 1), p. 12020, 2019.

R. Idroes, Muslem, Saiful, Mahmudi, G.M. Idroes, R. Suhendra, Irvanizam, Zamzami, P. M, Dead Time Determination of 2-Alkanone Homologues Series using Methanol/Water Eluent in High Performance Liquid Chromatography System by Indirect Method, IOP; Sci. Mater. Eng., 2019.

E. Sufriadi, H. Meilina, A.A. Munawar, R. Idroes, Fourier Transformed Infrared (FTIR) spectroscopy analysis of patchouli essential oils based on different geographical area in Aceh, IOP Conf. Ser. Mater. Sci. Eng., 2021.

Z. Akbar, R. Idroes, B. Ginting, T. Karma, S. Rahimah, Z. Helwani, M. Yusuf, Identification of Gayo arabic coffee beans and powder using the FTIR-PCA combination method, IOP Conf. Ser. Mater. Sci. Eng., 2021.

I. Ikhsan, R. Yusnaini, R. Nasution, A.A. Munawar, R. Idroes, Application of Near-Infrared spectroscopy and chemometric (PCA) in variety holothuria atra and holothuria scabra in Simeuleu, Aceh province, IOP Conf. Ser. Mater. Sci. Eng., 2021.

R. Idroes, M. Yusuf, Saiful, M. Alatas, Subhan, A. Lala, Muslem, R. Suhendra, G.. Idroes, Marwan, T.M.. Mahlia, Geochemistry Exploration and Geothermometry Application in the North Zone of Seulawah Agam, Aceh Besar District, Indonesia, Energies, 2019.

I. Husna, A. Rusyana, Muslem, G. M. Idroes, R. Suhendra and, and R. Idroes, Grouping of Retention Index on Gas Chromatography using Cluster Analysis, IOP Conf. Ser. Mater. Sci. Eng., Vol. 796, p. 012064, April 2020.

T.R. Noviandy, A. Maulana, N.R. Sasmita, R. Suhendra, Muslem, G.M. Idroes, M. Paristiowati, Z. Helwani, E. Yandri, S. Rahimah, Muhammad, Irvanizam, R. Idroes, The implementation of K-Means clustering in kovats retention index on gas chromatography, IOP Conf. Ser. Mater. Sci. Eng, 2021.

R. Todeschini and V. Consonni, Handbook of Molecular Descriptors. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2000.

S.G. Tumilaar, F. Fatimawali, N.J. Niode, Y. Effendi, R. Idroes, A.A. Adam, A. Rakib, T. Bin Emran, T.E. Tallei, The potential of leaf extract of Pangium edule Reinw as HIV-1 protease inhibitor: A computational biology approach, J. Appl. Pharm. Sci, 2020

T.E. Tallei, S.G. Tumilaar, N.J. Niode, Fatimawali, B.J. Kepel, R. Idroes, Y. Effendi, S.A. Sakib, T. Bin Emran, Potential of Plant Bioactive Compounds as SARS-CoV-2 Main Protease (Mpro) and Spike (S) Glycoprotein Inhibitors: A Molecular Docking Study, Scientifica. pp 1–18, 2020.

K. Khairan, R. Idroes, S.G. Tumilaar, T.E. Tallei, G.M. Idroes, F. Rahmadhany, M.U. Futri, N.M. Dinura, S. Mauliza, M. Diana, C.P. Maisarah, A. Maulana, T.R. Noviandy, R. Suhendra, Muslem, N. Earlia, Molecular docking study of fatty acids from Pliek U Oil in the inhibition of SARS-CoV-2 protein and enzymes, IOP Conf. Ser. Mater. Sci. Eng., 2021.

H. Noorizadeh, A. Farmany, and M. Noorizadeh, Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils, Quim. Nova, Vol. 34(no. 8) pp. 1398–1404, 2011.

R. Idroes, A. Maulana, T.R. Noviandy, R. Suhendra, N.R. Sasmita, A. Lala, Irvanizam, A Genetic Algorithm to Determine Research Consultation Schedules in Campus Environment, IOP Conf. Ser. Mater. Sci. Eng., Vol. 796, p. 012033, April 2020.

Sarkis, G., Georges, S., Slaoui, F., A Novel Algorithm for Smart Grids-Optimal Load Scheduling, (2018) International Review on Modelling and Simulations (IREMOS), 11 (2), pp. 67-75.

Dehnavifard, H., Radman, G., Kalyan, M., Design and Comparison of High-Speed Induction Machine and High-Speed Interior Permanent Magnet Machine, (2018) International Review on Modelling and Simulations (IREMOS), 11 (3), pp. 151-157.

Y. Wang, L. Mu, Y. He, Y. Tang, C. Liu, Y. Lu, L. Xu, Heat transfer analysis of blood perfusion in diabetic rats using a genetic algorithm, Microvasc. Res, 2020.

A. Pourrajabian, M. Dehghan, S. Rahgozar, Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one?, Sustain. Energy Technol. Assessments, 2021

E. Wirsansky, Hands-On Genetic Algorithms with Python, Packt Publishing, 2020.

M. Kuhn, K. Johnson, Feature engineering and selection: A practical approach for predictive models, CRC Press, 2019.

S. Riahi, M. R. Ganjali, E. Pourbasheer, and P. Norouzi, QSRR Study of GC Retention Indices of Essential-Oil Compounds by Multiple Linear Regression with a Genetic Algorithm, Chromatographia, Vol. 67(no. 11–12), pp. 917–922, June 2008.

Idroes, R., Noviandy, T., Maulana, A., Suhendra, R., Sasmita, N., Muslem, M., Idroes, G., Irvanizam, I., Retention Index Prediction of Flavor and Fragrance by Multiple Linear Regression and the Genetic Algorithm, (2019) International Review on Modelling and Simulations (IREMOS), 12 (6), pp. 373-380.

A. Maulana, T.R. Noviandy, R. Idroes, N.R. Sasmita, R. Suhendra, I. Irvanizam, Prediction of Kovats Retention Indices for Fragrance and Flavor using Artificial Neural Network, in: 2020 Int. Conf. Electr. Eng. Informatics, IEEE, 2020.

M. Aćimović, L. Pezo, V. Tešević, I. Čabarkapa, M. Todosijević, QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition, Ind. Crops Prod., Vol. 154, p. 112752, October 2020.

J. Stangierski, D. Weiss, A. Kaczmarek, Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese, Eur. Food Res. Technol., 2020.

S. Kalantary, A. Jahani, R. Jahani, MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications, Sci. Rep, 2020.

J. Arias, J. Mejía, Y. Córdoba, J.R. Martínez, E. Stashenko, J.M. del Valle, Optimization of flavonoids extraction from Lippia graveolens and Lippia origanoides chemotypes with ethanol-modified supercritical CO2 after steam distillation, Ind. Crops Prod., Vol. 146, p. 112170, April 2020.

E.E. Stashenko, J.R. Martínez, M.P. Cala, D.C. Durán, D. Caballero, Chromatographic and mass spectrometric characterization of essential oils and extracts from Lippia (Verbenaceae) aromatic plants, J. Sep. Sci., Vol. 36, no. 1, pp. 192–202, January 2013.

D.R. Oliveira, G.G. Leitão, P.D. Fernandes, S.G. Leitão, Ethnopharmacological studies of Lippia origanoides, Rev. Bras. Farmacogn., Vol. 24, no. 2, pp. 206–214, March 2014.

C. Hernandes, E.S. Pina, S.H. Taleb-Contini, B.W. Bertoni, I.M. Cestari, L.G. Espanha, E.A. Varanda, K.F.B. Camilo, E.Z. Martinez, S.C. França, A.M.S. Pereira, Lippia origanoides essential oil: an efficient and safe alternative to preserve food, cosmetic and pharmaceutical products, J. Appl. Microbiol., pp. 900–910, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., Vol. 12, pp. 2825–2830, 2011.

J.D. Hunter, Matplotlib: A 2D Graphics Environment, Comput. Sci. Eng., Vol. 9, no. 3, pp. 90–95, 2007.

I. Sushko, S. Novotarskyi, R. Körner, A.K. Pandey, M. Rupp, W. Teetz, S. Brandmaier, A. Abdelaziz, V. V. Prokopenko, V.Y. Tanchuk, R. Todeschini, A. Varnek, G. Marcou, P. Ertl, V. Potemkin, M. Grishina, J. Gasteiger, C. Schwab, I.I. Baskin, V.A. Palyulin, E. V. Radchenko, W.J. Welsh, V. Kholodovych, D. Chekmarev, A. Cherkasov, J. Aires-De-Sousa, Q.Y. Zhang, A. Bender, F. Nigsch, L. Patiny, A. Williams, V. Tkachenko, I. V. Tetko, Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information, J. Comput. Aided. Mol. Des., Vol. 25, no. 6, pp. 533–554, 2011.

V. V. Mihaleva, H.A. Verhoeven, R.C.H. de Vos, R.D. Hall, R.C.H.J. van Ham, Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index, Bioinformatics, Vol. 25, no. 6, pp. 787–794, March 2009. 787–794.

I.N. da Silva, D. Hernane Spatti, R. Andrade Flauzino, L.H.B. Liboni, S.F. dos Reis Alves, Artificial Neural Networks, Vol. 50(no. 2). Cham: Springer International Publishing, 2017.

Zhang, C.H. Zheng, Y. Xia, B. Wang, P. Chen, Optimization enhanced genetic algorithm-support vector regression for the prediction of compound retention indices in gas chromatography, Neurocomputing, Vol. 240, pp. 183–190, 2017.


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