Selin IŞIK, Abdullahi Garba USMAN, Sani Isah ABBA, Filiz MERİÇLİ

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.

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1. Mandal S, Mandal M. Coriander (Coriandrum sativum L.) essential oil: Chemistry and biological activity. Asian Pacific Journal of Tropical Biomedicine 2015; 5 (6): 421-428. doi: 10.1016/j.apjtb.2015.04.001
2. Emamghoreishi M, Khasaki M, Aazam MF.Coriandrum sativum: evaluation of its anxiolytic effect in the elevated plus-maze. Journal of Ethnopharmacology 2005; 96 (3): 365-370. doi: 10.1016/j.jep.2004.06.022
3. Erenler R, Telci İ, Elmastaş M, Akşit H, Gül F, et al. Quantification of flavonoids isolated fromtextit {Mentha spicata} in selected clones of Turkish mint landraces. Turkish Journal of Chemistry 2018; 42 (6): 1695-1705. doi:10.3906/kim-1712-3
4. Orhan I, Özçelik B, Aslan S, Kartal M, Karaoglu T, et al. Antioxidant and antimicrobial actions of the clubmoss Lycopodium clavatum L. Phytochemistry Reviews 2007; 6 (1): 189-196. doi: 10.1007/s11101-006-9053-x
5. Salem JH, Humeau C, Chevalot I, Harscoat-Schiavo C, Vanderesse R, et al. Effect of acyl donor chain length on isoquercitrin acylation and biological activities of corresponding esters. Process Biochemistry 2010; 45 (3): 382-389.
6. Yalçın FN, Kaya D, Çalış I, Ersöz T, Palaska E. Determination of iridoid glycosides from four Turkish Lamium species by HPLC-ESI/MS. Turkish Journal of Chemistry 2008; 32 (4): 457-467.
7. Tüzen M, Ozdemir M. Chromatographic determination of phenolic acids in the snowdrop by HPLC. Turkish Journal of Chemistry 2003; 27 (1): 49-54.
8. Ali TA, Mohamed GG, Aglan AA, Heakal FET. RP-HPLC Stability-indicating method for estimation of irbesartan and hydrochlorothiazide in bulk and pharmaceutical dosage form. Chinese Journal of Analytical Chemistry 2016; 44 (1): e1601-e1608. doi: 10.1016/S1872- 2040(16)60899-X
9. Lu J, Wang M, Wang Q, Li HP, Yang ZG. Determination of Benzotriazole and Its Derivatives in Aqueous Sample with Air-assisted LiquidLiquid Microextraction Followed by High-performance Liquid Chromatography. Chinese Journal of Analytical Chemistry 2018; 46 (4): e1817-e1825. doi: 10.1016/S1872-2040(17)61082-X
10. Wang XY, Chen XF, Gu YQ, Cao Y et al. Progress of Cell Membrane Chromatography and Its Application in Screening Active Ingredients of Traditional Chinese Medicine. Chinese Journal of Analytical Chemistry 2018; 46 (11): 1695-1702. doi: 10.1016/S1872-2040(18)61121-1
11. Lian XH, WangC, MengXS, BaiH et al. Determination of 10 Kinds of Caine-Type Prohibited Ingredients in Cosmetics by UltraPerformance Liquid Chromatography-Differential Mobility Spectrometry-Mass Spectrometry. Chinese Journal of Analytical Chemistry 2018; 47: 756-764. doi: 10.1016/S1872-2040(19)61160-6
12. Veenaas C, Linusson A, HaglundP.Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants. Analytical and Bioanalytical Chemistry 2018; 410 (30): 7931-7941. doi: 10.1007/s00216-018-1415x
13. XiongXC, Fang X, Ou YZ, Jiang Y et al. Artificial neural networks for classification and identification of data of biological tissue obtained by mass-spectrometry imaging. Chinese Journal of Analytical Chemistry 2012; 40 (1): 43-49. doi: 10.1016/S1872-2040(11)60525-2
14. Yu K, Cheng Y. Discriminating the genuineness of Chinese medicines using least squares support vector machines. Chinese Journal of Analytical Chemistry 2006; 34 (4): 561-564. doi: 10.1016/S1872-2040(06)60029-7
15. Yi-Ming BI, Guo-Hai CHU, Ji-ZhongWU, Yuan KL, Wu J et al. Ensemble partial least squares algorithm based on variable clustering for quantitative infrared spectrometric analysis. Chinese Journal of Analytical Chemistry 2015; 43 (7): 1086-1091. doi: 10.1016/S1872- 2040(15)60842-8
16. Amini I, Pal K, Esmaeilpoor S, AbdelkarimA. Prediction of two-dimensional gas chromatography time-of-flight mass spectrometry retention times of 160 pesticides and 25 environmental organic pollutants in grape by multivariate chemometrics methods. Advanced Journal of Chemistry-Section A2018; 1 (1): 12-31. doi: 10.29088/SAMI/AJCA.2018.3.1231
17. Korany MA, Mahgoub H, Fahmy OT, Maher HM. Application of artificial neural networks for response surface modelling in HPLC method development. Journal of Advanced Research 2012; 3 (1): 53-63. doi: 10.1016/j.jare.2011.04.001
18. Ng BK, Tan TT, Shellie RA, Dicinoski GW,Haddad PR. Computer-assisted simulation and optimisation of retention in ion chromatography. TrAC Trends in Analytical Chemistry 2016; 80: 625-635. doi: 10.1016/j.trac.2015.07.015
19. D’Archivio AA. Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients. Molecules 2019; 24 (3): 632. doi: 10.3390/molecules24030632
20. Khademi F, Jamal SM, Deshpande N, Londhe S. Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment 2016; 5 (2): 355-369. doi: 10.1016/j.ijsbe.2016.09.003
21. Elkiran G, Nourani V,Abba SI. Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology 2019; 577: 123962. doi: 10.1016/j.jhydrol.2019.123962
22. Granata F, Papirio S, Esposito G, Gargano R, De Marinis G. Machine learning algorithms for the forecasting of wastewater quality indicators. Water 2017; 9(2): 105. doi: 10.3390/w9020105
23. Solgi A, Pourhaghi A, Bahmani R, Zarei H. Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD). Ecohydrology & Hydrobiology 2017; 17 (2): 164-175. doi: 10.1016/j. ecohyd.2017.02.002
24. Abba SI, Pham QB, Usman AG, Linh NTT, Aliyu DS. et al. Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant. Journal of Water Process Engineering 2020; 33: 101081. doi: 10.1016/j. jwpe.2019.101081
25. Usman AG, Isik S, Abba SI. A Novel Multi - model Data - Driven Ensemble Technique for the Prediction of Retention Factor in HPLC Method Development. Chromatographia 2020; 83: 933–945. doi: 10.1007/s10337-020-03912-0
26. Abba SI, Usman AG, Isik S. Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach. Chemometrics and Intelligent Laboratory Systems 2020; 201. doi: 10.1016/j.chemolab.2020.104007
27. Kim Y, Narayanan S, Chang KO. Inhibition of influenza virus replication by plant-derived isoquercetin. Antiviral Research 2010; 88 (2): 227-235. doi: 10.1016/j.antiviral.2010.08.016
28. Eisinga R, Te Grotenhuis M, Pelzer B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown. International Journal of Public Health 2013; 58 (4): 637-642.doi: 10.1007/s00038-012-0416-3
29. Sheela KG, Deepa SN. Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Mathematical Problems in Engineering 2013; 2013: 425740. doi: 10.1155/2013/425740
30. Pham QB, Abba SI, Usman AG, Linh NTT, Gupta V et al. Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall. Water Resources Management 2019; 33 (15): 5067-5087. doi: 10.1007/s11269-019-02408-3
31. Elkiran G, Nourani V, Abba SI, Abdullahi J. Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global Journal of Environmental Science and Management 2018; 4 (4): 439-450. doi: 10.22034/GJESM.2018.04.005
32. Guddat S, Solymos E, Orlovius A, Thomas A et al. High‐throughput screening for various classes of doping agents using a new ‘dilute‐and‐ shoot’liquid chromatography‐tandem mass spectrometry multi‐target approach. Drug Testing and Analysis 2011; 3 (11-12): 836-850. doi: 10.1002/dta.372
33. Ruggieri F, D’Archivio AA, Carlucci G, Mazzeo P. Application of artificial neural networks for prediction of retention factors of triazine herbicides in reversed-phase liquid chromatography. Journal of Chromatography A 2005; 1076 (1-2): 163-169. doi: 10.1016/j. chroma.2005.04.03