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Application of artificial neural networks in the geographical identification of coffee samples

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Abstract

The recognition of the characteristics of coffee associated with a given agricultural system and aimed at adding value and attending the consumers’ demands stimulates the production of types of coffee properly described. The objective of this study was to explore and to explain the physicochemical characteristics and sensory attributes of the coffee grown in Parana State (Southern Brazil) based on an integrated approach of the terrior and the application of artificial neural network. Physicochemical variables of green coffee beans and roasted coffee beans were determined, as well as sensory attributes of the beverage. One hundred and seventy-two coffee samples were analyzed for moisture, proteins, chlorogenic acids, tannins, total acidity, total lipids, caffeine, total and reducing sugars and minerals. These properties were tabulated and presented to artificial neural network multilayer perceptron to be identified as the region and the city of planting. The artificial neural network classified correctly and tested 100% of the samples grown by region. For the database containing information by city, the automatic mode of the software Statistica 9.0 was used. The neural network showed 99% accuracy in training and 100% accuracy in the stage of testing and validation.

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References

  1. Bishop CM (1995) Neural networks for pattern recognition. University Oxford, Oxford

    Google Scholar 

  2. Haykin S (2001) Redes neurais: princípios e práticas. Bookman, Porto Alegre

    Google Scholar 

  3. Hair JF Jr, Anderson RE, Tatham RL, Black WC (2005) Análise fatorial. In: Análise Multivariada de Dados, 5ª ed. Bookman, Porto Alegre

  4. Melssen W, Wehrens R, Buydens L (2006) Supervised Kohonen networks for classification problems. Chemom Intell Lab Syst 83:99–113

    Article  CAS  Google Scholar 

  5. Kovács ZL (1996) Redes neurais Artificiais: Fundamentos e Aplicações. Acadêmica, São Paulo

    Google Scholar 

  6. Mukesh D (1996) Applications of neural computing for process chemists: I. Introduction to neural network. J Chem Educ 73:431–433

    Article  CAS  Google Scholar 

  7. Linder R, Pöppl SJ (2003) A new neural network approach classifies olfactory signals with high accuracy. Food Qual Preference 14:435–440

    Article  Google Scholar 

  8. Deisingh AK, Stone DC, Thompson M (2004) Applications of electronic noses and tongues in food analysis. Int J Food Sci Technol 39:587–604

    Article  CAS  Google Scholar 

  9. Liao S (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Systems Appl 28:93–109

    Article  Google Scholar 

  10. Hammond MH, Riedel CJ, Rose-Pehrsson SL, Williams FW (2004) Training set optimization methods for a probabilistic neural network. Chem Intell Lab Syst 71:73–78

    Article  CAS  Google Scholar 

  11. Braga AP, Carvalho ACPLF, Ludermir TB (2000) Redes Neurais Artificiais: Teoria e Aplicações, 1st edn. LTC, Rio de Janeiro

    Google Scholar 

  12. Azevedo FM, Brasil LM, Oliveira RCL (2000) As Redes Neurais com Aplicações em Controle e em Sistemas Especialistas, 1st edn. Bookstores, Florianópolis

    Google Scholar 

  13. Hunter A, Kenedy L, Henry J, Fergunsom I (2000) Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Methods Programs Biomed 62:11–19

    Article  CAS  Google Scholar 

  14. Serra F, Guillou CG, Reniero F, Ballarin L, Cantagallo MI, Wieser M, Iyer SS, Héberger K, Vanhaecke F (2005) Determination of geographic origin of green coffee by component analysis of carbon, nitrogen and boron stable isotope ratios. Rapid Commun Mass Spectrom 19:2011–2015

    Google Scholar 

  15. Anderson K, Smith BW (2002) Chemical profiling to differentiate geographic growing origins of coffee. J Agric Food Chem 50:2068–2075

    Article  CAS  Google Scholar 

  16. Bodt E, Cottrell M, Verleysen M (2002) Statistical tools to assess the reliability of self-organizing maps. Neural Netw 15:967–978

    Article  Google Scholar 

  17. Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8:211–226

    Article  Google Scholar 

  18. Ludermir TB, Yamazaki A, Zanchetin C (2006) Ann optimization methodology for neural networks weights and architectures. IEEE Trans Knowl Data Eng 17:1452–1459

    Google Scholar 

  19. Windeatt T (2006) Accuracy/diversity and ensemble MLP classifier design. IEEE Trans Neural Netw 17:1194–1211

    Article  Google Scholar 

  20. StatSoft, Inc (2009) STATISTICA (data analysis software system), version 9.0. http://www.statsoft.com

  21. Maeztu L, Sanz C, Andueza S, De Penã MP, Bello J (2001) Characterization of espresso coffee aroma by static headspace gc-ms and sensory flavor profile. J Agric Food Chem 49:5437–5444

    Article  CAS  Google Scholar 

  22. De Maria CAB, Moreira RFA, Trugo LC (1999) Componentes voláteis do café torrado. Parte I: compostos heterocíclicos. Quim Nova 22:209–217

    Google Scholar 

  23. Grosch W (2001) Evaluation of the key odorants of foods by dilute experiments, aroma models and omission. Chem Senses 26:533–545

    Article  CAS  Google Scholar 

  24. Mazzafera P (1999) Chemical composition of defective coffee beans. Food Chem 64:547–554

    Article  CAS  Google Scholar 

  25. Clifford MN (1985) Chemical and physical aspects of green coffee and coffee products. In: Clifford MN, Wilson KC (eds) Coffee, botany, biochemistry and production of beans and beverage. Croom Helm, London, pp 305–359

    Google Scholar 

  26. Carvalho VD, Chagas SJR, Chalfoun SM, Botrel N, Juste ESG (1994) Relação entre a composição físico-química e química do grão beneficiado e a qualidade de bebida do café. Pesq Agropec Bras 29:449–454

    Google Scholar 

  27. BRASIL. Ministério da Agricultura Pecuária e Abastecimento. Instrução Normativa n° 8 de 11 June 2003

  28. Instituto Adolfo Lutz. Métodos físicos químicos para análise de alimentos. Edição IV, 1ª edição digital, São Paulo, 2008

  29. Clifford MN, Wight JC (1976) Chorogenic acids and other cinnamates nature, occurrence and dietary burden. J Sci Food Agric 27:73–84

    Article  CAS  Google Scholar 

  30. AOAC—Association of Official Agricultural Chemists (1990) Official methods of analysis, 15th edn. Washington

  31. Southgate DAT (1976) Determination of food carbohydrates. Applied Science Publishers, Londres

    Google Scholar 

  32. Pavan MA, Bloch MDM, Zempulsk HC, Miyazawa M, Zocoler DC (1992) Manual de análises químicas de solo e controle da qualidade. IAPAR, Londrina, Circular Técnica, n. 76

  33. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  CAS  Google Scholar 

  34. Broyden CG (1970) The convergence of a class of double rank minimization algorithms: 2. The new algorithm. J Appl Math 6:222–231

    Google Scholar 

  35. Fletcher R (1970) A new approach to variable metric algorithms. Comput J 13:317–322

    Article  Google Scholar 

  36. Goldfarb D (1970) A family of variable metric methods derived by variational means. J Comput Math 24:23–26

    Article  Google Scholar 

  37. Shanno DF (1970) Conditioning of quasi-Newton methods for function minimization. Math Comput 24:647–650

    Article  Google Scholar 

  38. Dai YH (2002) Convergence properties of the BFGS algoritm. SIAM J Optim 13:693–701

    Article  Google Scholar 

  39. Fernandes SM, Pinto NAVD, Thé PMP, Pereira RGFA, Carvalho VD (2001) Teores de polifenóis, ácido clorogênico, cafeína e proteína em café torrado. Rev Bras Agrociência 7:197–199

    Google Scholar 

  40. Sarrazin C, Lequére JL, Gretsch C, Liardon R (2000) Representativeness of coffee aroma extracts: a comparison of different extraction methods. Food Chem 70:99–106

    Article  CAS  Google Scholar 

  41. Illy A, Viani R (1996) Espresso coffee: The chemistry of quality, 2nd edn. Academic press, San Diego

    Google Scholar 

  42. Sivetz M, Desrosier NW (1979) Physical and chemical aspects of coffee. Coffee Technology, Westpor

    Google Scholar 

  43. Ginz M, Balzer HH, Bradbury AGW, Maier HG (2000) Formation of aliphatic acids by carbohydrate degradation during roasting of coffee. Eur Food Res Tech 211:404–410

    Article  CAS  Google Scholar 

  44. Rogers WJ, Michaux S, Bastin M, Bucheli P (1999) Changes to the content of sugar, sugar alcohols, myo-inositol, carboxylic acid and inorganic anions in developing grains from different varieties of Robusta (Coffea canephora) and arabica (Coffea arabica) coffees. Plant Sci 149:115–123

    Article  CAS  Google Scholar 

  45. Vasconcelos AL, Franca AS, Gloria MBA, Mendonça JCF (2007) A comparative study of chemical attributes and levels of amines in defective green and roasted coffee beans. Food Chem 100:26–32

    Article  Google Scholar 

  46. Farah A, Monteiro MC, Calado V, Franca AS, Trugo LC (2006) Correlation between cup quality and chemical attributes of Brazilian coffee. Food Chem 98:373–380

    Article  CAS  Google Scholar 

  47. Moon JK, Yoo HS, Shibamoto T (2009) Role of roasting conditions in the level of chlorogenic acid content in coffee beans: correlation with coffee acidity. J Agric Food Chem 57:5365–5369

    Article  CAS  Google Scholar 

  48. Jiang D, Peterson DG (2010) Role of hydroxycinnamic acids in food flavor: a brief overview. Phytochem Rev 9:187–193

    Article  CAS  Google Scholar 

  49. Frank O, Blumberg S, Kunert C, Zehentbauer G, Hofmann T (2007) Structure determination and sensory analysis of bitter-tasting 4-vinylcatechol oligomers and their identification in roasted coffee by means of LC-MS/MS. J Agric Food Chem 55:1945–1954

    Article  CAS  Google Scholar 

  50. Montavon P, Duruz E, Rumo G, Pratz G (2003) Evolution of green coffee protein profiles with maturation and relationship to coffee cup quality. J Agric Food Chem 51:2328–2334

    Article  CAS  Google Scholar 

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Acknowledgments

The authors are grateful to Fundação Araucária, UEL and IAPAR for their financial support.

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Authors and Affiliations

  1. Department of Chemistry, State University of Londrina, P. O. Box 6001, Londrina, PR, 86051-990, Brazil

    Dionísio Borsato, Marcos Vinicios Roberto Pina & Kelly Roberta Spacino

  2. Parana Agronomic Institute (IAPAR), P. O. Box 481, Londrina, PR, 86001-970, Brazil

    Maria Brígida dos Santos Scholz & Armando Androcioli Filho

Authors
  1. Dionísio Borsato
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  2. Marcos Vinicios Roberto Pina
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  3. Kelly Roberta Spacino
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  4. Maria Brígida dos Santos Scholz
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  5. Armando Androcioli Filho
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Correspondence to Dionísio Borsato.

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Borsato, D., Pina, M.V.R., Spacino, K.R. et al. Application of artificial neural networks in the geographical identification of coffee samples. Eur Food Res Technol 233, 533–543 (2011). https://doi.org/10.1007/s00217-011-1548-z

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  • DOI: https://doi.org/10.1007/s00217-011-1548-z

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