Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/27833
Title: Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments
Authors: Guiné, Raquel P. F. 
Barroca, Maria João 
Gonçalves, Fernando J. 
Alves, Mariana 
Oliveira, Solange 
Mendes, Mateus 
Keywords: Antioxidant activity; Banana; Drying; Neural network; Phenolic compounds
Issue Date: 1-Feb-2015
Publisher: Elsevier
Citation: GUINÉ, Raquel P. F. [et. al] - Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. "Food Chemistry". ISSN 0308-8146. Vol. 168 (2014) p. 454–459
Abstract: 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.
URI: http://hdl.handle.net/10316/27833
ISSN: 0308-8146
DOI: 10.1016/j.foodchem.2014.07.094
Rights: openAccess
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
Artificial neural network modelling.pdf445.86 kBAdobe PDFView/Open
Show full item record

SCOPUSTM   
Citations

42
checked on Apr 12, 2019

WEB OF SCIENCETM
Citations

20
checked on May 11, 2018

Page view(s) 50

337
checked on May 14, 2019

Download(s) 5

3,089
checked on May 14, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.