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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]

 

AUTHOR(S)

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

 

KEYWORDS

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

 

ABSTRACT

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.

 

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