Details

Autor(en) / Beteiligte

• Rodrigues, B.U.
• Soares, A.S.
• Costa, R.M.
• Van Baalen, J.
• Salvini, R.L.
• Silva, F.A.
• Caliari, M.
• Cardoso, K.C.R.
• Ribeiro, T.I.M.
• Delbem, A.C.B.
• Federson, F.M.
• Coelho, C.J.
• Laureano, G.T.
• Lima, T.W.

Titel

A feasibility cachaca type recognition using computer vision and pattern recognition

Ist Teil von

• Computers and electronics in agriculture, 2016-04, Vol.123, p.410-414

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •The problem of recognition of aging time and wood type in chacaca is presented.•A new approach is introduced using a computer vision system.•The developed image capture device and information processing method is presented.•Results show that the new technique is cheaper and better than previous approaches. 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 1–3years, 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 3years 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.