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Orientador(es)
Resumo(s)
The present work used an artificial neural network (ANN) model to correlate beetroot extraction conditions with total
phenolic compounds (TPC), anthocyanins (ANT) and antioxidant activity (AOA). The input variables were extraction time,
type of solvent, solvent volume/sample mass (VMR = volume to mass ratio) and order of extraction. The ANN models
produced showed very good accuracy (R>94%), being suitable for data mining using weight analysis techniques. The
experiments involved variable conditions: solvents (methanol, ethanol: water and acetone: water), extraction times (15 and
60 min), VMR (5, 10 and 20), order of extract (3 sequential extractions). The TPC were evaluated by the Folin-Ciocalteu
method, ANT by the SO 2 bleaching method and AOA following the ABTS method. The experimental results showed that the
extracting solutions used in this study exhibited similar extraction efficiency for TPC, but not for AOA. Also, the results
allowed concluding that a higher VMR originated extracts with higher amounts of TPC and AOA.
Descrição
Palavras-chave
Beetroot Phenolic compounds
Contexto Educativo
Citação
Guiné RPF, Mendes M, Gonçalves F (2019) Optimization of bioactive compound’s extraction conditions from beetroot by means of artificial neural networks (ANN). Agricultural Engineering International: CIGR Journal, 21(4), 216-223