Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network

• Autor(a) principal: Anástacio, Ana
• Data de Publicação: 2016
• Outros Autores: Silva, Ruben, Carvalho, Isabel Saraiva de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/9175

Sweet potato peels (SPP) are a major waste generated during root processing and currently have little commercial value. Phenolics with free radical scavenging activity from SPP may represent a possible added-value product for the food industry. The aqueous extraction of phenolics from SPP was studied using a Central Composite Design with solvent to solid ratio (30-60 mL g(-1)), time (30-90 min) and temperature (25-75 A degrees C) as independent variables. The comparison of response surface methodology (RSM) and artificial neural network (ANN) analysis on extraction modelling and optimising was performed. Temperature and solvent to solid ratio, alone and in interaction, presented a positive effect in TPC, ABTS and DPPH assays. Time was only significant for ABTS assay with a negative influence both as main effect and in interaction with other independent variables. RSM and ANN models predicted the same optimal extraction conditions as 60 mL g(-1) for solvent to solid ratio, 30 min for time and 75 A degrees C for temperature. The obtained responses in the optimized conditions were as follow: 11.87 +/- 0.69 mg GAE g(-1) DM for TPC, 12.91 +/- 0.42 mg TE g(-1) DM for ABTS assay and 46.35 +/- 3.08 mg TE g(-1) DM for DPPH assay. SPP presented similar optimum extraction conditions and phenolic content than peels of potato, tea fruit and bambangan. Predictive models and the optimized extraction conditions offers an opportunity for food processors to generate products with high potential health benefits.