Optimization of Antioxidant Properties of Creams with Berry Extracts by Artificial Neural Networks

Oxidative stress and the excess of free radicals accelerate the ageing process of human skin. The application of skin cream with antioxidant compounds could reduce the damage caused by free radicals. In this work we studied two types of skin creams with extracts from aronia (Aronia melanocarpa), elderberry (Sambucus nigra) and bilberry (Vaccinium myrtillus) because of their high content of anthocyanins, i.e. strong natural antioxidants. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability of the skin creams with berry extracts were studied with ESR spectroscopy. The artificial neural networks were applied to optimize the berry extract concentration and storage time for oil-in-water and water-in-oil creams. Based on experimental results chokeberry and elderberry extracts in oil-in-water cream base revealed higher DPPH radical scavenging ability than in the corresponding water-in-oil. Artificial neural networks predicts maxima of DPPH radical scavenging for 1-week stored elderberry (2.23 mg DPPH/g) and 1-week stored chokeberry (5.84 mg DPPH/g) and bilberry (5.26 mg DPPH/g) 0.76% extracts in oil-in-water creams. The maxima of DPPH radical scavenging for water-in-oil creams were predicted for 6-week stored 0.8% aronia extract, freshly prepared 0.76% bilberry extract and 1-week stored 0.56% elderberry extract. The artificial neural networks predicted values are in good agreement with the experimental values. DPPH-EPR could be combined with artificial neural networks to optimize the extract concentration, and the type of cream base as well as to predict the effect of storage based on a limited number of experiments and samples.