Publication
Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks
| dc.contributor.author | Guiné, Raquel | |
| dc.contributor.author | Matos, Susana | |
| dc.contributor.author | Gonçalves, Fernando J. | |
| dc.contributor.author | Costa, Daniela | |
| dc.contributor.author | Mendes, Mateus | |
| dc.date.accessioned | 2018-02-28T13:32:42Z | |
| dc.date.available | 2019-03-01T01:30:12Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | The study aimed at evaluating the influence of different production conditions, conservation and extraction procedures on the total phenolic compounds and antioxidant activity of blueberries by DPPH and ABTS methods. The production factors considered were origin, altitude of the farm location and age of the bushes. The conservation conditions considered were freezing as opposed to the fresh product. The extraction procedures included two different solvents and two orders of extraction. The data analysis was carried out by training artificial neural networks to model the data and extract information from the model. The results obtained revealed that the type of extract and the order of extraction influenced the concentrations of phenolic compounds as well as the antioxidant activity of the different samples studied. Also the origin of the farms from where the blueberries were harvested significantly influence those properties, showing that the blueberries from Oliveira do Hospital had less phenolic compounds and lower antioxidant activity. Also older bushes at higher altitudes seem to produce berries richer in these properties. Regarding conservation, no influence was observed for phenols but a slight influence could be detected for antioxidant activity. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Guiné, R.P.F., Matos, S., Gonçalves, F.J., Costa, D., & Mendes, M. (2018). Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks. International Journal of Fruit Science, 18(2), 199-214. doi:10.1080/15538362.2018.1425653 | pt_PT |
| dc.identifier.doi | 10.1080/15538362.2018.1425653 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.19/4857 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Taylor & Francis | pt_PT |
| dc.relation.publisherversion | https://www.tandfonline.com/doi/abs/10.1080/15538362.2018.1425653 | pt_PT |
| dc.subject | Antioxidant activity | pt_PT |
| dc.subject | ANN modeling | pt_PT |
| dc.subject | Extraction | pt_PT |
| dc.subject | Production conditions | pt_PT |
| dc.subject | Storage | pt_PT |
| dc.subject | Total phenols | pt_PT |
| dc.title | Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 214 | pt_PT |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 199 | pt_PT |
| oaire.citation.title | International Journal of Fruit Science | pt_PT |
| oaire.citation.volume | 18 | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | article | pt_PT |