Publication
Cachaça Classification Using Chemical Features and Computer Vision
| dc.contributor.author | Rodrigues, Bruno Urbano | |
| dc.contributor.author | Costa, Ronaldo Martins da | |
| dc.contributor.author | Salvini, Rogério Lopes | |
| dc.contributor.author | Soares, Anderson da Silva | |
| dc.contributor.author | Silva, Flávio Alves da | |
| dc.contributor.author | Caliari, Márcio | |
| dc.contributor.author | Cardoso, Karla Cristina Rodrigues | |
| dc.contributor.author | Ribeiro, Tânia Isabel Monteiro | |
| dc.date.accessioned | 2018-02-02T12:00:43Z | |
| dc.date.available | 2018-02-02T12:00:43Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | Cacha¸ca is a type of distilled drink from sugarcane with great economic importance. Its classification includes three types: aged, premium and extra premium. These three classifications are related to the aging time of the drink in wooden casks. Besides the aging time, it is important to know what the wood used in the barrel storage in order the properties of each drink are properly informed consumer. This paper shows a method for automatic recognition of the type of wood and the aging time using information from a computer vision system and chemical information. Two algorithms for pattern recognition are used: artificial neural networks and k-NN (k-Nearest Neighbor). In the case study, 144 cacha¸ca samples were used. The results showed 97% accuracy for the problem of the aging time classification and 100% for the problem of woods classification. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Rodrigues, Bruno Urbano; Costa, Ronaldo Martins da; Salvini, Rogério Lopes; Soares, Anderson da Silva; Silva, Flávio Alves da; Caliari, Márcio; Cardoso, Karla Cristina Rodrigues; Ribeiro, Tânia Isabel Monteiro (2014). In 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, QLD. Australia. p. 2024-2033 | pt_PT |
| dc.identifier.doi | 10.1016/j.procs.2014.05.186 | pt_PT |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | http://hdl.handle.net/10198/15508 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Pattern recognition | pt_PT |
| dc.subject | Drink analysis | pt_PT |
| dc.subject | Computer vision | pt_PT |
| dc.title | Cachaça Classification Using Chemical Features and Computer Vision | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 2033 | pt_PT |
| oaire.citation.startPage | 2024 | pt_PT |
| oaire.citation.title | Procedia Computer Science | pt_PT |
| oaire.citation.volume | 29 | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |