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Prediction of Ascorbic Acid Dissolution by UV-Vis Spectroscopy for Oral Dispersible Film (ODF) Using Artificial Neural Network (ANN)

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Abstract:

Oral dispersible film (ODF) containing ascorbic acid (AA) was synthesised using the electrospinning process, and its dissolving behaviour was analysed by Ultraviolet-Visible (UV-Vis) spectroscopy. The obtained time, wavelength and absorbance data were applied to train an Artificial Neural Network (ANN) using the Levenberg-Marquardt algorithm. A total of 42 datasets were separated into training (90%), validation (5%) and testing (5%) sections. The ANN model displayed good predictive ability, giving a low mean squared error (MSE) and a regression coefficient (R=1), demonstrating a significant correlation between predicted and experimental dissolution profiles. These results demonstrate that ANN can efficiently predict ODF dissolution profiles, hence lowering experimental burden and boosting efficiency in pharmaceutical formulation research.

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Periodical:

Materials Science Forum (Volume 1189)

Pages:

83-90

DOI:

https://doi.org/10.4028/p-5xijnV

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Online since:

May 2026

Authors:

Fetriya Fatihah Nasar*, Noor Fitrah Abu Bakar, Nur Asyikin Ahmad Nazri, Jamaluddin Mahmud

Keywords:

Artificial Neural Network (ANN), Electrospinning, Oral Dispersible Film (ODF), Ultraviolet-Visible Spectroscopy (UV-VIS)

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© 2026 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

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