Details

Autor(en) / Beteiligte

• Samson, Shazwani
• Basri, Mahiran
• Fard Masoumi, Hamid Reza
• Abdul Malek, Emilia
• Abedi Karjiban, Roghayeh
• Mukherjee, Amitava

Titel

An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide

Ist Teil von

• PloS one, 2016-07, Vol.11 (7), p.e0157737-e0157737

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2016

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.