Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments

• Autor(a) principal: Guiné, Raquel P. F.
• Data de Publicação: 2015
• Outros Autores: Barroca, Maria João, Gonçalves, Fernando J., Alves, Mariana, Oliveira, Solange, Mendes, Mateus
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/27833; https://doi.org/10.1016/j.foodchem.2014.07.094
• topic: Antioxidant activity; Banana; Drying; Neural network; Phenolic compounds

Bananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70 °C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds.