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This study has reported comparative effects of silver nanoparticles  (AgNPs),  sucrose and sodium chloride as osmotic solutions on antioxidant activity and microbial quality of 10 mm tomato slices. 40 g of tomato slices were dehydrated osmotically (OD) at different temperatures (60, 70 and 80 °C) and time (30, 60, 90, 120 150 and 180 min).Water loss, solid  impregnation, water and solid diffusivities of tomato slices were found to increase with increase in solution temperatures and concentrations with AgNPs having the greatest influence. Antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl increased with increase in solution concentrations but decreased with increase in temperature. Three-wayANOVA(R2=0.998) revealed additive statistically significant effects of osmotic agents,  concentrations and temperatures on antioxidant activity;   F(8,54)=67.854,P=0.00. Polynomial regression analysis with response surface methodology validated experiments such that for each unit increase in concentration and temperature, antioxidant activity increased with good coefficients of determination; sucrose (R2 = 0.87), NaCl, (R2 = 0.89) and AgNPs (R2 = 0.91). Potato dextrose and nutrient agars were used for isolating and identifying microorganisms in OD tomato slices. Tomato slices dehydrated with AgNPs had the highest microbial inhibition of fungi with growth occurring after 7 days, unlike in treatments with sucrose and NaCl where fungal growth appeared after 2 and 5 days, respectively. Aspergillus niger was the most prevalent fungus. It can be concluded that AgNPs may serve as a viable means to dehydrate and preserve tomatoes without loss of antioxidant activity. Keywords : Osmotic dehydration, polynomial regression, response surface, antioxidant activity, three-way ANOVA, silver nanoparticles.

sa journal of chemistry/south african journal of chemistry

Comparative effects of silver nanoparticles, sucrose and sodium chloride as osmotic solutions for tomato slices: antioxidant activity, microbial quality and modelling with polynomial regression model