Browsing by Author "Costa, Ronaldo Martins da"
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- Cachaça Classification Using Chemical Features and Computer VisionPublication . 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 MonteiroCacha¸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.
- A feasibility cachaca type recognition using computer vision and pattern recognitionPublication . Rodrigues, Bruno Urbano; Soares, Anderson da Silva; Costa, Ronaldo Martins da; Van Baalen, J.; Salvini, Rogério Lopes; Silva, Flávio Alves da; Caliari, Márcio; Cardoso, Karla Cristina Rodrigues; Ribeiro, Tânia Isabel Monteiro; Delbem, A.C.B.; Federson, F.M.; Coelho, C.J.; Laureano, G.T.; Lima, T.W.Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.