Chemical components retention and modelling of antioxidant activity using neural networks in oven dried tomato slices with and without osmotic dehydration pre-treatment

Abstract

This study investigated the comparative retention of chemical and bioactive components in oven dried tomato slices with and without 10% saline osmotic dehydration (OD) pre-treatment. Antioxidant activity of tomato slices was modelled from phytochemical parameters using artificial neural networks (ANNs) trained with multiplayer perceptron (MLP). Increase in water loss, solid gain, weight reduction and performance ratio obtained for tomato slices in 10% saline osmo-active solution indicated that moisture diffusion occurred faster than solid impregnation. Higher moisture diffusivity in slices with OD pre-treatment suggested faster water removal. A two-way ANOVA conducted revealed statistically significant effects of treatment methods, immersion time intervals and their synergistic interactions on moisture, ash, carbohydrate, energy, minerals (except sodium), antioxidant activity, flavonoid, lycopene, β-carotene contents. Statistical significance of both effects with non-significant and non-additive interactions were obtained for crude fat, crude protein, fatty acids, crude fibre and total phenols. Antioxidant activities modelled with ANNs had MLP architectures of 4-3-1 (R2 = 0.992) and 4-3-2-1 (R2 = 0.995) for oven dried tomato slices with and without OD pre-treatment respectively. High correlation coefficients R2 = 0.998 (without OD pre-treatment) and R2 = 0.999 (with OD pre-treatment) were obtained between experimentally determined antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl and modelled antioxidant activity using ANNs.