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...
Gespeichert in:
| Veröffentlicht in: | Computers and electronics in agriculture Jg. 123; S. 410 - 414 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.04.2016
|
| Schlagworte: | |
| ISSN: | 0168-1699, 1872-7107 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| 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. |
|---|---|
| AbstractList | •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 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. 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. |
| Author_xml | – sequence: 1 givenname: B.U. surname: Rodrigues fullname: Rodrigues, B.U. organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 2 givenname: A.S. surname: Soares fullname: Soares, A.S. email: anderson@inf.ufg.br organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 3 givenname: R.M. surname: Costa fullname: Costa, R.M. organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 4 givenname: J. surname: Van Baalen fullname: Van Baalen, J. organization: University of Wyoming, Computer Science Department, USA – sequence: 5 givenname: R.L. surname: Salvini fullname: Salvini, R.L. organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 6 givenname: F.A. surname: Silva fullname: Silva, F.A. organization: Federal University of Goiás, Food Engineering School, Brazil – sequence: 7 givenname: M. surname: Caliari fullname: Caliari, M. organization: Federal University of Goiás, Food Engineering School, Brazil – sequence: 8 givenname: K.C.R. surname: Cardoso fullname: Cardoso, K.C.R. organization: Federal University of Goiás, Food Engineering School, Brazil – sequence: 9 givenname: T.I.M. surname: Ribeiro fullname: Ribeiro, T.I.M. organization: Polytechnic Institute of Bragança, Portugal – sequence: 10 givenname: A.C.B. surname: Delbem fullname: Delbem, A.C.B. organization: University of São Paulo, Institute of Mathematical Science and Computation, Brazil – sequence: 11 givenname: F.M. surname: Federson fullname: Federson, F.M. organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 12 givenname: C.J. surname: Coelho fullname: Coelho, C.J. organization: Pontifical Catholic University of Goiás, School of Sciences and Computing, Brazil – sequence: 13 givenname: G.T. surname: Laureano fullname: Laureano, G.T. organization: Federal University of Goiás, Institute of Computer Science, Brazil – sequence: 14 givenname: T.W. surname: Lima fullname: Lima, T.W. organization: Federal University of Goiás, Institute of Computer Science, Brazil |
| BookMark | eNqNkE1LxDAQhoMouH78Aw89emmdNG3SehBE_ALBi55DmkzXLN20Jllh_70p60E8qKdhZt53XuY5IvtudEjIGYWCAuUXq0KP60ktizJ1BbACStgjC9qIMhcUxD5ZpEWTU962h-QohBWkvm3EgrxcZz2qYDs72LjNtNJvSqssbifMPOpx6Wy0o8s2wbplNsdsIvrsw4Z5qpzJJhXTxH1Xn5CDXg0BT7_qMXm9u325ecifnu8fb66fcl1VVczLzrAOODcoWkax5E3X6VqbrkdoAKuG9xpMI4zgXa1EXRvWgugoMDC1qYEdk_Pd3cmP7xsMUa5t0DgMyuG4CZI2jPOalkL8Q5oiW1aVNEkvd1LtxxA89lLbqOa_old2kBTkTF2u5I66nKlLYDJRT-bqh3nydq389i_b1c6GCdeHRS-Dtug0Gpu4RmlG-_uBT8dGoSA |
| CitedBy_id | crossref_primary_10_1016_j_compag_2020_105460 crossref_primary_10_1016_j_eswa_2017_06_044 crossref_primary_10_1002_jsfa_11857 crossref_primary_10_3390_beverages5040062 crossref_primary_10_1016_j_postharvbio_2018_01_013 crossref_primary_10_1080_10408347_2018_1548926 crossref_primary_10_3390_molecules25133025 crossref_primary_10_1016_j_foodcont_2025_111493 crossref_primary_10_1016_j_foodchem_2025_143554 crossref_primary_10_1016_j_foodchem_2018_02_035 |
| Cites_doi | 10.1016/j.compag.2013.01.013 10.1016/j.compag.2013.08.027 10.1016/j.foodres.2005.07.005 10.3923/jai.2013.210.219 10.1002/jms.1197 10.1016/j.compag.2014.08.008 10.1016/j.procs.2014.05.186 10.1007/s10044-009-0148-z 10.1021/jf0511190 10.1016/S0023-6438(95)80008-5 10.1016/j.compag.2014.07.009 10.1016/j.aca.2010.09.039 10.1016/j.asoc.2011.12.019 10.1007/s11947-012-0867-9 10.1016/j.compag.2014.06.003 10.1007/s12293-014-0144-8 |
| ContentType | Journal Article |
| Copyright | 2016 Elsevier B.V. |
| Copyright_xml | – notice: 2016 Elsevier B.V. |
| DBID | AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D 7S9 L.6 |
| DOI | 10.1016/j.compag.2016.03.020 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Civil Engineering Abstracts AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| EISSN | 1872-7107 |
| EndPage | 414 |
| ExternalDocumentID | 10_1016_j_compag_2016_03_020 S0168169916300898 |
| GeographicLocations | Brazil |
| GeographicLocations_xml | – name: Brazil |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ 9JM 9JN AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO AAYFN ABBOA ABBQC ABFNM ABFRF ABGRD ABJNI ABKYH ABLVK ABMAC ABMZM ABRWV ABXDB ABYKQ ACDAQ ACGFO ACGFS ACIUM ACIWK ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADQTV AEBSH AEFWE AEKER AENEX AEQOU AESVU AEXOQ AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV AJRQY ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ANZVX AOUOD ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CBWCG CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLV HLZ HVGLF HZ~ IHE J1W KOM LCYCR LG9 LW9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 QYZTP R2- RIG ROL RPZ SAB SBC SDF SDG SES SEW SNL SPC SPCBC SSA SSH SSV SSZ T5K UHS UNMZH WUQ Y6R ~G- ~KM 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABWVN ACIEU ACLOT ACMHX ACRPL ACVFH ADCNI ADNMO ADSLC AEIPS AEUPX AFJKZ AFPUW AGQPQ AGWPP AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D 7S9 L.6 |
| ID | FETCH-LOGICAL-c444t-2bd3b066de7931e268bbc5cdbfe080e486fc0d87d76b5a755d3907b1030d5d503 |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000375166400043&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0168-1699 |
| IngestDate | Sun Sep 28 01:56:02 EDT 2025 Wed Oct 01 12:04:13 EDT 2025 Sat Nov 29 03:16:18 EST 2025 Tue Nov 18 21:41:29 EST 2025 Fri Feb 23 02:29:53 EST 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Computer vision Drinks Pattern recognition |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c444t-2bd3b066de7931e268bbc5cdbfe080e486fc0d87d76b5a755d3907b1030d5d503 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | http://hdl.handle.net/10198/15503 |
| PQID | 1808093421 |
| PQPubID | 23500 |
| PageCount | 5 |
| ParticipantIDs | proquest_miscellaneous_1836651277 proquest_miscellaneous_1808093421 crossref_citationtrail_10_1016_j_compag_2016_03_020 crossref_primary_10_1016_j_compag_2016_03_020 elsevier_sciencedirect_doi_10_1016_j_compag_2016_03_020 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-04-01 |
| PublicationDateYYYYMMDD | 2016-04-01 |
| PublicationDate_xml | – month: 04 year: 2016 text: 2016-04-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Computers and electronics in agriculture |
| PublicationYear | 2016 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| 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 |
| SSID | ssj0016987 |
| Score | 2.1872604 |
| 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... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 410 |
| SubjectTerms | Aroma automatic detection Brazil chemical analysis Classification Classifiers Computer vision Consumption Drinks flavor Laboratory equipment odors Pattern recognition prices rum wavelet Wavelet transforms Wood |
| Title | A feasibility cachaca type recognition using computer vision and pattern recognition |
| URI | https://dx.doi.org/10.1016/j.compag.2016.03.020 https://www.proquest.com/docview/1808093421 https://www.proquest.com/docview/1836651277 |
| Volume | 123 |
| WOSCitedRecordID | wos000375166400043&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7107 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016987 issn: 0168-1699 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWlgMcEE9RXjISt1WiJHbs5JhWRYBEhWCL9mY5trNtVSXVdrcqv4K_zCR-bFSgpQcuUWI5Vna_zzNjex4IvVOpJpIoFSkOs4k2RRIVKleRJKDcjOpNgnooNsEPDor5vPwymfz0sTAXp7xti8vL8uy_Qg1tAHYfOnsLuMOg0AD3ADpcAXa4_hPw1bQx0jm9_pgqqY6kknarNXgLAeRrH247FHWY2hhzmzZgSLnZjnuPTVhfB8Imd96U0Rkca-Vi6ZJ5BMJ87fTyeLG24mg3PozDpk7Xxz4Nwin-Flr3Om_Qxp9D43eQQrsSdJmNJInHexUpG7m4uO1LBmtWZksiBfmbkZEEpc7L1bgn-kc5b7ccTuLBUX_Re-ixIVdtlmz0mj_Lv6LughOi9287EXYU0Y8iEiJglDtoO-N5CZJ-u_q4P_8UDqZYWdgIfPdDfDTm4DL4-9f8zdq5ovcHY2b2ED1wqxBcWfY8QhPTPkb3qw14T9CswiMeYccj3PMIj5iBBx5hzyNseYSBGtjxaNz7KTp8vz_b-xC5EhyRopSuoqzWpAarVBuQ46nJWFHXMJV13RhYahhasEYluuCaszqXPM81KRNe97XrdK7zhDxDW23XmucIFznjDTOZzA2jPKeS1HVhSg2qGpbpstxBxP9VQrn89H2ZlFNxHVA7KApvndn8LDf05x4F4WxMazsKoNYNb771oAkQwf25mmxNtz4XaZ-btSQ0S6_rQxgD45rzF7f84pfo3mYqvUJbq-XavEZ31cXq-Hz5xvHzF_xHuRs |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+feasibility+cachaca+type+recognition+using+computer+vision+and+pattern+recognition&rft.jtitle=Computers+and+electronics+in+agriculture&rft.au=Rodrigues%2C+B.U.&rft.au=Soares%2C+A.S.&rft.au=Costa%2C+R.M.&rft.au=Van+Baalen%2C+J.&rft.date=2016-04-01&rft.issn=0168-1699&rft.volume=123&rft.spage=410&rft.epage=414&rft_id=info:doi/10.1016%2Fj.compag.2016.03.020&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_compag_2016_03_020 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0168-1699&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0168-1699&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0168-1699&client=summon |