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Computers and Electronics in Agriculture
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
DBLP
Article . 2016
Data sources: DBLP
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A feasibility cachaca type recognition using computer vision and pattern recognition

Authors: Bruno Urbano Rodrigues; Anderson da Silva Soares; Ronaldo Martins da Costa; Jeffrey Van Baalen; Rogerio Lopes Salvini; F. A. Silva; Márcio Caliari; +7 Authors

A feasibility cachaca type recognition using computer vision and pattern recognition

Abstract

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 cachaca) 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.

Country
Portugal
Keywords

Pattern recognition, Computer vision, Drinks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Top 10%
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bronze
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