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IJSTR >> Volume 9 - Issue 8, August 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Development Of Computational Intelligence Algorithms For Modelling The Performance Of Humanin And Its Derivatives In HPLC Optimization Method Development

[Full Text]



Umar Muhammad Ghali, Mohamed Alhosen Ali Degm, Ahmed Nouri Alsharksi, Qendresa Hoti, Abdullahi Garba Usman



Humanin; HPLC; ANFIS; MLR; retention time; resolution



Humanin and its derivatives are considered as neural cells protecting agents against pathological proteins such as the amyloid protein precursor that causes the alzheimer’s disorder. The precise prediction of the properties of humanin in high performance liquid chromatography (HPLC) optimization method is of paramount importance. Therefore, to achieve this the development of resilient and satisfactory computational intelligence tools is crucial. In the current study, the comparative potential performance of adaptive neurofuzzy inference system (ANFIS) and multilinear regression models. The outputs given by the ANFIS and MLR models were compared with the experimental values through two statistical evaluation indices Nash-Sutcliffe efficiency (NC) and Mean squared error (MSE). Graphical illustrations such as scatter plot and time series were employed to compare the performance of the models. The results of the study indicated that ANFIS outperformed MLR for predicting the maximum retention time (tR max) and resolution of humanin and its derivatives in HPLC optimization method development. Equally, ANFIS showed the highest value of NC (0.9999/ 0.9992) for tR max and (0.9998/ 0.9994) for resolution in the training and testing stages respectively. Similarly, ANFIS indicated lowest values of MSE for tR max and resolution in both the training and testing stages. The comparative analysis of the result demonstrated that ANFIS as a promising non-linear artificial intelligence based model found to be more reliable and suitable for predicting the performance of humanin and its derivatives in HPLC optimization method development.



[1] K. Novotná, J. Havliš, and J. Havel, “Optimisation of high performance liquid chromatography separation of neuroprotective peptides: Fractional experimental designs combined with artificial neural networks,” J. Chromatogr. A, vol. 1096, no. 1–2, pp. 50–57, 2005.
[2] C. Veenaas, A. Linusson, and P. Haglund, “Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants,” Anal. Bioanal. Chem., vol. 410, no. 30, pp. 7931–7941, 2018.
[3] L. R. S. and J. W. Dolan, “High‐Performance Gradient Elution: The Practical Application of the Linear‐Solvent‐Strength Model.” John Wiley & Sons.
[4] S. Agatonovic-Kustrin, M. Zecevic, and L. Zivanovic, “Use of ANN modelling in structure-retention relationships of diuretics in RP-HPLC,” J. Pharm. Biomed. Anal., vol. 21, no. 1, pp. 95–103, 1999.
[5] J. Zeng, Q. Chai, X. Peng, and S. Li, “Geographical Origin Identification for Tetrastigma Hemsleyanum Based on High Performance Liquid Chromatographic Fingerprint,” Proc. - 2019 Chinese Autom. Congr. CAC 2019, pp. 1816–1820, 2019.
[6] S. Agatonovic-Kustrin, M. Zecevic, L. j. Zivanovic, and I. G. Tucker, “Application of artificial neural networks in HPLC method development,” J. Pharm. Biomed. Anal., vol. 17, no. 1, pp. 69–76, 1998.
[7] S. I. Abba, A. G. Usman, and S. Işik, “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemom. Intell. Lab. Syst., p. 104007, 2020.
[8] H. I. El-shorbagy, F. Elsebaei, S. F. Hammad, and A. M. El-brashy, “Optimization and modeling of a green dual detected RP-HPLC method by UV and fluorescence detectors using two level full factorial design for simultaneous determination of sofosbuvir and ledipasvir : Application to average content and uniformity of dosage ,” Microchem. J., vol. 147, no. February, pp. 374–392, 2019.
[9] M. A. Aslam et al., “Nano Biomed Eng SVM Based Classification and Prediction System for Gastric Cancer Using Dominant Features of Saliva,” vol. 12, no. 1, pp. 1–13, 2020.
[10] M. M. Aboulwafa et al., “Journal of Pharmaceutical and Biomedical Analysis Authentication and discrimination of green tea samples using UV – vis , FTIR and HPLC techniques coupled with chemometrics analysis,” J. Pharm. Biomed. Anal., vol. 164, pp. 653–658, 2019.
[11] M. Celeste, E. Galvão, B. Rosa, E. Ferreira, R. Silva, and C. Caldas, “Screening of Mangifera indica L . functional content using PCA and neural networks ( ANN ),” Food Chem., vol. 273, no. December 2017, pp. 115–123, 2019.
[12] J. Tomi et al., “Chemometrically Assisted RP-HPLC Method Development for Efficient Separation of Ivabradine and its Eleven Impurities,” vol. 32, no. June 2019, pp. 53–63, 2020.
[13] E. Science, “Evaluation of quality of Salvia miltiorrhiza Bunge from different provenances by HPLC-DAD fingerprint combined with Chemometrics Method Evaluation of quality of Salvia miltiorrhiza Bunge from different provenances by HPLC-DAD fingerprint combined with Che,” 2019.
[14] M. Reclo, E. Yilmaz, Y. Bazel, and M. Soylak, “Switchable solvent based liquid phase microextraction of palladium coupled with determination by flame atomic absorption spectrometry,” Int. J. Environ. Anal. Chem., vol. 97, no. 14–15, pp. 1315–1327, 2017.
[15] M. A. Korany, H. Mahgoub, O. T. Fahmy, and H. M. Maher, “Application of artificial neural networks for response surface modelling in HPLC method development,” J. Adv. Res., vol. 3, no. 1, pp. 53–63, 2012.
[16] J. Yang, G. Xu, H. Kong, Y. Zheng, T. Pang, and Q. Yang, “Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer,” J. Chromatogr. B Anal. Technol. Biomed. Life Sci., vol. 780, no. 1, pp. 27–33, 2002.
[17] V. Nourani, H. Hakimzadeh, and A. B. Amini, “Implementation of artificial neural network technique in the simulation of dam breach hydrograph,” J. Hydroinformatics, vol. 14, no. 2, p. 478, 2012.
[18] G. Zhang, B. Eddy Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: The state of the art,” Int. J. Forecast., vol. 14, no. 1, pp. 35–62, 1998.
[19] S. I. Abba et al., “Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant,” J. Water Process Eng., vol. 33, no. October 2019, p. 101081, 2020.
[20] F. Khademi and K. Behfarnia, “Evaluation of Concrete Compressive Strength Using Artificial Neural Network and Multiple Linear Regression Models,” Iust, vol. 6, no. 3, pp. 423–432, 2016.
[21] Q. B. Pham et al., “Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall,” Water Resour. Manag., vol. 33, no. 15, 2019.
[22] K. Zarei, M. Atabati, and M. Ahmadi, “Shuffling cross–validation–bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography,” J. Environ. Sci. Heal. - Part B Pestic. Food Contam. Agric. Wastes, vol. 52, no. 5, pp. 346–352, 2017.
[23] P. Kazemi et al., “Computational intelligence modeling of granule size distribution for oscillating milling,” Powder Technol., vol. 301, pp. 1252–1258, 2016.
[24] H. Sanikhani and O. Kisi, “River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches,” Water Resour. Manag., vol. 26, no. 6, pp. 1715–1729, 2012.
[25] E. Dehghanian, M. Kaykhaii, and M. Mehrpur, “Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction,” Monatshefte fur Chemie, vol. 146, no. 8, pp. 1217–1227, 2015.
[26] S. H. Park et al., “Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model,” J. Chromatogr. A, vol. 1486, pp. 68–75, 2017.