A feasibility cachaca type recognition using computer vision and pattern recognition
•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 app...
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| Vydáno v: | Computers and electronics in agriculture Ročník 123; s. 410 - 414 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2016
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| ISSN: | 0168-1699, 1872-7107 |
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| 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 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. |
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| AbstractList | 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. •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. 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. |
| Author | Salvini, R.L. Rodrigues, B.U. Ribeiro, T.I.M. Lima, T.W. Federson, F.M. Silva, F.A. Delbem, A.C.B. Costa, R.M. Van Baalen, J. Coelho, C.J. Caliari, M. Cardoso, K.C.R. Soares, A.S. Laureano, G.T. |
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| References | Rodríguez-Pulido, Gordillo, González-Miret, Heredia (b0090) 2013; 99 Kashiha, Bahr, Ott, Moons, Niewold, Ödberg, Berckmans (b0040) 2013; 93 Pathare, Opara, Al-Said (b0060) 2013; 6 Reinhard, Khan, Akyz, Johnson (b0080) 2008 Sujatha, Punithavalli, Thavavel (b0100) 2013; 6 Kashiha, Bahr, Ott, Moons, Niewold, Ödberg, Berckmans (b0045) 2014; 107 Schaefer, Krawczyk, Celebi, Iyatomi (b0095) 2014; 6 Zhang, Ma (b0110) 2012 Fernández-González, Montejo-Bernardo, Rodríguez-Prieto, Castaño-Monllor, Badía-Laíño, Díaz-García (b0030) 2014; 108 Mallat (b0055) 1999 de Souza, Augusti, Catharino, Siebald, Eberlin, Augusti (b0015) 2007; 42 Petrosian, Meyer (b0065) 2013; vol. 19 Duarte-Mermoud, Beltrán, Bustos (b0025) 2010; 13 Pontes, Santos, Araujo, Almeida, Lima, Gaiao, Souto (b0075) 2006; 39 Rodrigues, da Costa, Salvini, da Silva Soares, da Silva, Caliari, Ribeiro (b0085) 2014; 29 Brand-Williams, Cuvelier, Berset (b0005) 1995; 28 De Souza, Vásquez, del Mastro, Acree, Lavin (b0010) 2006; 54 Kongsro (b0050) 2014; 109 Gevers, Gijsenij, Van de Weijer, Geusebroek (b0035) 2012; vol. 23 AOAC INTERNATIONAL (b9000) 1997; vol. 2 Yu, Zhang, Yu, Domeniconi, You, Han (b0105) 2012; 12 Do Brasil, S.F., 1973. Decreto no 73.267, 06 de dezembro de 1973. Pinto, Galvão, Araújo (b0070) 2010; 682 Gevers (10.1016/j.compag.2016.03.020_b0035) 2012; vol. 23 Kongsro (10.1016/j.compag.2016.03.020_b0050) 2014; 109 AOAC INTERNATIONAL (10.1016/j.compag.2016.03.020_b9000) 1997; vol. 2 Duarte-Mermoud (10.1016/j.compag.2016.03.020_b0025) 2010; 13 Schaefer (10.1016/j.compag.2016.03.020_b0095) 2014; 6 De Souza (10.1016/j.compag.2016.03.020_b0010) 2006; 54 Sujatha (10.1016/j.compag.2016.03.020_b0100) 2013; 6 10.1016/j.compag.2016.03.020_b0020 Zhang (10.1016/j.compag.2016.03.020_b0110) 2012 Pontes (10.1016/j.compag.2016.03.020_b0075) 2006; 39 Rodríguez-Pulido (10.1016/j.compag.2016.03.020_b0090) 2013; 99 de Souza (10.1016/j.compag.2016.03.020_b0015) 2007; 42 Yu (10.1016/j.compag.2016.03.020_b0105) 2012; 12 Reinhard (10.1016/j.compag.2016.03.020_b0080) 2008 Fernández-González (10.1016/j.compag.2016.03.020_b0030) 2014; 108 Rodrigues (10.1016/j.compag.2016.03.020_b0085) 2014; 29 Mallat (10.1016/j.compag.2016.03.020_b0055) 1999 Kashiha (10.1016/j.compag.2016.03.020_b0040) 2013; 93 Petrosian (10.1016/j.compag.2016.03.020_b0065) 2013; vol. 19 Pathare (10.1016/j.compag.2016.03.020_b0060) 2013; 6 Brand-Williams (10.1016/j.compag.2016.03.020_b0005) 1995; 28 Pinto (10.1016/j.compag.2016.03.020_b0070) 2010; 682 Kashiha (10.1016/j.compag.2016.03.020_b0045) 2014; 107 |
| References_xml | – volume: 29 start-page: 2024 year: 2014 end-page: 2033 ident: b0085 article-title: Cachaça classification using chemical features publication-title: Procedia Comput. Sci. – volume: 6 start-page: 210 year: 2013 ident: b0100 article-title: Applicability of ensemble clustering and ensemble classification algorithm for user navigation pattern prediction publication-title: J. Artif. Intell. – volume: 13 start-page: 181 year: 2010 end-page: 188 ident: b0025 article-title: Chilean wine varietal classification using quadratic fisher transformation publication-title: Pattern Anal. Appl. – volume: 93 start-page: 111 year: 2013 end-page: 120 ident: b0040 article-title: Automatic identification of marked pigs in a pen using image pattern recognition publication-title: Comput. Electron. Agric. – volume: 54 start-page: 485 year: 2006 end-page: 488 ident: b0010 article-title: Characterization of cachaça and rum aroma publication-title: J. Agric. Food Chem. – volume: 6 start-page: 233 year: 2014 end-page: 240 ident: b0095 article-title: An ensemble classification approach for melanoma diagnosis publication-title: Memetic Comput. – volume: 99 start-page: 108 year: 2013 end-page: 115 ident: b0090 article-title: Analysis of food appearance properties by computer vision applying ellipsoids to colour data publication-title: Comput. Electron. Agric. – volume: vol. 19 year: 2013 ident: b0065 publication-title: Wavelets in Signal and Image Analysis: From Theory to Practice – year: 2012 ident: b0110 article-title: Ensemble Machine Learning – volume: 28 start-page: 25 year: 1995 end-page: 30 ident: b0005 article-title: Use of a free radical method to evaluate antioxidant activity publication-title: {LWT} – Food Sci. Technol. – volume: 108 start-page: 166 year: 2014 end-page: 172 ident: b0030 article-title: Easy-to-use analytical approach based on ATR–FTIR and chemometrics to identify apple varieties under Protected Designation of Origin (PDO) publication-title: Comput. Electron. Agric. – reference: Do Brasil, S.F., 1973. Decreto no 73.267, 06 de dezembro de 1973. – volume: 42 start-page: 1294 year: 2007 end-page: 1299 ident: b0015 article-title: Differentiation of rum and brazilian artisan cachaça via electrospray ionization mass spectrometry fingerprinting publication-title: J. Mass Spectrom. – year: 2008 ident: b0080 article-title: Color Imaging: Fundamentals and Applications – volume: 6 start-page: 36 year: 2013 end-page: 60 ident: b0060 article-title: Colour measurement and analysis in fresh and processed foods: a review publication-title: Food Bioprocess Technol. – volume: vol. 23 year: 2012 ident: b0035 publication-title: Color in Computer Vision: Fundamentals and Applications – volume: 109 start-page: 32 year: 2014 end-page: 35 ident: b0050 article-title: Estimation of pig weight using a Microsoft Kinect prototype imaging system publication-title: Comput. Electron. Agric. – volume: 39 start-page: 182 year: 2006 end-page: 189 ident: b0075 article-title: Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry publication-title: Food Res. Int. – volume: vol. 2 start-page: 1 year: 1997 end-page: 43 ident: b9000 publication-title: Official Methods of Analysis – volume: 107 start-page: 38 year: 2014 end-page: 44 ident: b0045 article-title: Automatic weight estimation of individual pigs using image analysis publication-title: Comput. Electron. Agric. – volume: 682 start-page: 37 year: 2010 end-page: 47 ident: b0070 article-title: Ensemble wavelet modelling for determination of wheat and gasoline properties by near and middle infrared spectroscopy publication-title: Anal. Chimica Acta – volume: 12 start-page: 1511 year: 2012 end-page: 1522 ident: b0105 article-title: Semi-supervised ensemble classification in subspaces publication-title: Appl. Soft Comput. – year: 1999 ident: b0055 article-title: A Wavelet Tour of Signal Processing – volume: 93 start-page: 111 year: 2013 ident: 10.1016/j.compag.2016.03.020_b0040 article-title: Automatic identification of marked pigs in a pen using image pattern recognition publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2013.01.013 – volume: 99 start-page: 108 year: 2013 ident: 10.1016/j.compag.2016.03.020_b0090 article-title: Analysis of food appearance properties by computer vision applying ellipsoids to colour data publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2013.08.027 – year: 2008 ident: 10.1016/j.compag.2016.03.020_b0080 – volume: 39 start-page: 182 issue: 2 year: 2006 ident: 10.1016/j.compag.2016.03.020_b0075 article-title: Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry publication-title: Food Res. Int. doi: 10.1016/j.foodres.2005.07.005 – ident: 10.1016/j.compag.2016.03.020_b0020 – volume: 6 start-page: 210 issue: 3 year: 2013 ident: 10.1016/j.compag.2016.03.020_b0100 article-title: Applicability of ensemble clustering and ensemble classification algorithm for user navigation pattern prediction publication-title: J. Artif. Intell. doi: 10.3923/jai.2013.210.219 – year: 2012 ident: 10.1016/j.compag.2016.03.020_b0110 – volume: 42 start-page: 1294 issue: 10 year: 2007 ident: 10.1016/j.compag.2016.03.020_b0015 article-title: Differentiation of rum and brazilian artisan cachaça via electrospray ionization mass spectrometry fingerprinting publication-title: J. Mass Spectrom. doi: 10.1002/jms.1197 – year: 1999 ident: 10.1016/j.compag.2016.03.020_b0055 – volume: vol. 2 start-page: 1 year: 1997 ident: 10.1016/j.compag.2016.03.020_b9000 – volume: 109 start-page: 32 year: 2014 ident: 10.1016/j.compag.2016.03.020_b0050 article-title: Estimation of pig weight using a Microsoft Kinect prototype imaging system publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2014.08.008 – volume: 29 start-page: 2024 year: 2014 ident: 10.1016/j.compag.2016.03.020_b0085 article-title: Cachaça classification using chemical features publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2014.05.186 – volume: 13 start-page: 181 issue: 2 year: 2010 ident: 10.1016/j.compag.2016.03.020_b0025 article-title: Chilean wine varietal classification using quadratic fisher transformation publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-009-0148-z – volume: vol. 23 year: 2012 ident: 10.1016/j.compag.2016.03.020_b0035 – volume: vol. 19 year: 2013 ident: 10.1016/j.compag.2016.03.020_b0065 – volume: 54 start-page: 485 issue: 2 year: 2006 ident: 10.1016/j.compag.2016.03.020_b0010 article-title: Characterization of cachaça and rum aroma publication-title: J. Agric. Food Chem. doi: 10.1021/jf0511190 – volume: 28 start-page: 25 issue: 1 year: 1995 ident: 10.1016/j.compag.2016.03.020_b0005 article-title: Use of a free radical method to evaluate antioxidant activity publication-title: {LWT} – Food Sci. Technol. doi: 10.1016/S0023-6438(95)80008-5 – volume: 108 start-page: 166 year: 2014 ident: 10.1016/j.compag.2016.03.020_b0030 article-title: Easy-to-use analytical approach based on ATR–FTIR and chemometrics to identify apple varieties under Protected Designation of Origin (PDO) publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2014.07.009 – volume: 682 start-page: 37 issue: 1 year: 2010 ident: 10.1016/j.compag.2016.03.020_b0070 article-title: Ensemble wavelet modelling for determination of wheat and gasoline properties by near and middle infrared spectroscopy publication-title: Anal. Chimica Acta doi: 10.1016/j.aca.2010.09.039 – volume: 12 start-page: 1511 issue: 5 year: 2012 ident: 10.1016/j.compag.2016.03.020_b0105 article-title: Semi-supervised ensemble classification in subspaces publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2011.12.019 – volume: 6 start-page: 36 issue: 1 year: 2013 ident: 10.1016/j.compag.2016.03.020_b0060 article-title: Colour measurement and analysis in fresh and processed foods: a review publication-title: Food Bioprocess Technol. doi: 10.1007/s11947-012-0867-9 – volume: 107 start-page: 38 year: 2014 ident: 10.1016/j.compag.2016.03.020_b0045 article-title: Automatic weight estimation of individual pigs using image analysis publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2014.06.003 – volume: 6 start-page: 233 issue: 4 year: 2014 ident: 10.1016/j.compag.2016.03.020_b0095 article-title: An ensemble classification approach for melanoma diagnosis publication-title: Memetic Comput. doi: 10.1007/s12293-014-0144-8 |
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| Snippet | •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... Brazilian rum (also known as cachaca) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters... Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters... |
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| Title | A feasibility cachaca type recognition using computer vision and pattern recognition |
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