title: Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.
abstract: Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.
authors.affiliation: Pharmaceutical Chemistry Department, Faculty of Pharmacy, University of Tabuk, Tabuk, Kingdom of Saudi Arabia. mme-87@hotmail.com.|Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt. mme-87@hotmail.com.
authors.forename: Mahmoud M
authors.initials: MM
authors.lastname: Elkhoudary
authors.affiliation: Pharmaceutical Chemistry Department, Faculty of Pharmacy, University of Tabuk, Tabuk, Kingdom of Saudi Arabia.|Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Alshaheed Shehata Ahmad Hegazy St, Beni-Suef, 62514, Egypt.
authors.forename: Ibrahim A
authors.initials: IA
authors.lastname: Naguib
authors.affiliation: Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt.
authors.forename: Randa A
authors.initials: RA
authors.lastname: Abdel Salam
authors.affiliation: Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt.
authors.forename: Ghada M
authors.initials: GM
authors.lastname: Hadad
keywords: Agomelatine; Linear ANN; Linear SVR; Machines learning; Principle component; Spectrofluorimetry; Stability