Journal of Food Science and Technology

, Volume 55, Issue 7, pp 2457–2466 | Cite as

Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses

  • Douglas Fernandes BarbinEmail author
  • Leonardo Fonseca Maciel
  • Carlos Henrique Vidigal Bazoni
  • Margareth da Silva Ribeiro
  • Rosemary Duarte Sales Carvalho
  • Eliete da Silva Bispo
  • Maria da Pureza Spínola Miranda
  • Elisa Yoko Hirooka
Original Article


Effective and fast methods are important for distinguishing cocoa varieties in the field and in the processing industry. This work proposes the application of NIR spectroscopy as a potential analytical method to classify different varieties and predict the chemical composition of cocoa. Chemical composition and colour features were determined by traditional methods and then related with the spectral information by partial least-squares regression. Several mathematical pre-processing methods including first and second derivatives, standard normal variate and multiplicative scatter correction were applied to study the influence of spectral variations. The results of chemical composition analysis and colourimetric measurements show significant differences between varieties. NIR spectra of samples exhibited characteristic profiles for each variety and principal component analysis showed different varieties in according to spectral features.


Chemical composition Chocolate Principal component analysis Cocoa beans NIR spectroscopy PLS regression 



The authors gratefully acknowledge the financial support from the Coordination for the Improvement of Higher Education Personnel (CAPES) strategic research initiative under the Brazilian Ministry of Education, Project Number 23038.019085/2009-14. This research was supported by Sao Paulo Research Foundation (FAPESP), Young Researchers Award, Grant Number 2015/24351-2. Professor Elisa Yoko Hirooka is a CNPq research fellow.


  1. Alvarez C, Perez E, Cros E, Lares M, Assemat S, Boulanger R, Davrieux F (2012) The use of near infrared spectroscopy to determine the fat, caffeine, theobromine and (-)-epicatechin contents in unfermented and sun-dried beans of criollo cocoa. J Near Infrared Spectrosc 20:307–315CrossRefGoogle Scholar
  2. AOAC (1995) Official methods of analysis, 16th edn. Association of Official Analytical Chemists, WashingtonGoogle Scholar
  3. Barbin DF, ElMasry G, Sun D-W, Allen P (2012) Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal Chim Acta 719:30–42CrossRefPubMedPubMedCentralGoogle Scholar
  4. Barbin DF, ElMasry G, Sun D-W, Allen P (2013) Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem 138:1162–1171CrossRefPubMedGoogle Scholar
  5. Barbin DF, Kaminishikawahara CM, Soares AL, Mizubuti IY, Grespan M, Shimokomaki M, Hirooka EY (2015) Prediction of chicken quality attributes by near infrared spectroscopy. Food Chem 168:554–560CrossRefPubMedGoogle Scholar
  6. Barnes RJ, Dhanoa MS, Lister SJ, Susan J (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. J Appl Spectrosc 43(5):772–777CrossRefGoogle Scholar
  7. Bazoni CH, Ida EI, Barbin DF, Kurozawa LE (2017) Near-infrared spectroscopy as a rapid method for evaluation physicochemical changes of stored soybeans. J Stored Prod 73:1–6CrossRefGoogle Scholar
  8. Burger J, Geladi P (2006) Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst 131:1152–1160CrossRefPubMedGoogle Scholar
  9. Copikova J, Novotna M, Smidova I, Synytsya A, Cerna M (2003) Application of near infrared spectroscopy in chocolate analysis. Chemcké Listy 97:571–575Google Scholar
  10. Dagnew MD, Crowe TG, Schoenau JJ (2004) Measurement of nutrients in Saskatchewan hog manures using near-infrared spectroscopy. Can Biosyst Eng 46:33–37Google Scholar
  11. Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ (1994) The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. J Near Infrared Spectrosc 2(1):43–47CrossRefGoogle Scholar
  12. Faber NM, Rajkó R (2007) How to avoid over-fitting in multivariate calibration—the conventional validation approach and an alternative. Anal Chim Acta 595:98–106CrossRefPubMedGoogle Scholar
  13. Fearn T, Riccioli C, Garrido-Varo A, Guerrero-Ginel JE (2009) On the geometry of SNV and MSC. Chemometr Intell Lab 96(1):22–26CrossRefGoogle Scholar
  14. ISO (2016). ISO 3310-1 Test sieves—technical requirements and testing—part 1 test sieves of metal wire clothGoogle Scholar
  15. Jakubıkova M, Sadecka J, Kleinova A, Majek P (2016) Near-infrared spectroscopy for rapid classification of fruit spirits. J Food Sci Technol 53(6):2797–2803CrossRefPubMedPubMedCentralGoogle Scholar
  16. Kaffka KJ, Norris KH, Kulcsar F, Draskovits I (1982) Attempts to determine fat, protein and carbohydrate content in cocoa powder by the NIR technique. Acta Aliment 11:271–288Google Scholar
  17. Krahmer A, Engel A, Kadow D, Ali N, Umaharan P, Kroh LW, Schulz H (2015) Fast and neat—determination of biochemical quality parameters in cocoa using near infrared spectroscopy. Food Chem 181:152–159CrossRefPubMedGoogle Scholar
  18. Leite PB, Maciel LF, Opretzka LCF, Soares SE, Bispo ES (2013) Phenolic compounds, methylxanthines and antioxidant activity in cocoa mass and chocolates produced from “witch broom disease” resistant and non resistant cocoa cultivars. Cienc e Agrotec 37(3):244–250CrossRefGoogle Scholar
  19. Li G, Ren Y, Ren X, Zhang X (2015) Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS. J Food Sci Technol 52(1):258–266CrossRefPubMedGoogle Scholar
  20. Liu T, Zhou Y, Zhu Y, Song M, Li B-B, Shi Y, Gong J (2015) Study of the rapid detection of γ-aminobutyric acid in rice wine based on chemometrics using near infrared spectroscopy. J Food Sci Technol 52(8):5347–5351CrossRefPubMedGoogle Scholar
  21. Madalozzo ES, Sauer E, Nagata N (2015) Determination of fat, protein and moisture in ricotta cheese by near infrared spectroscopy and multivariate calibration. J Food Sci Technol 52(3):1649–1655CrossRefPubMedGoogle Scholar
  22. Martens H, Naes T (1989) Multivariate calibration. Wiley, ChichesterGoogle Scholar
  23. Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118CrossRefGoogle Scholar
  24. Osborne BG, Fearn T (1986) Near infrared spectroscopy in food analysis. Wiley, New YorkGoogle Scholar
  25. Osborne BG, Fearn T, Hindle PH (1993) Pratical NIR spectroscopy: with applications in food and beverage analysis. Longman Scientific & Technical, Harlow, pp 227Google Scholar
  26. Pizarro C, Esteban-Diez I, Nistal AJ, Gonzalez-Saiz JM (2004) Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy. Anal Chim Acta 509(2):217–227CrossRefGoogle Scholar
  27. Reis N, França AS, Oliveira LS (2013) Discrimination between roasted coffee, roasted corn and coffee husks by diffuse reflectance infrared fourier transform spectroscopy. Food Sci Technol 50:715–722Google Scholar
  28. Skibsted ETS, Boelens HFM, Westerhuis JA, Witte DT, Smilde AK (2004) New indicator for optimal preprocessing and wavelength selection of near-infrared spectra. J Appl Spectrosc 58(3):264–271CrossRefGoogle Scholar
  29. Sunoj S, Igathinathane C, Visvanathan R (2016) Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy. Comput Electron Agric 124:234–242CrossRefGoogle Scholar
  30. Teye E, Huang XY, Lei W, Dai H (2014) Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans. Food Res Int 55:288–293CrossRefGoogle Scholar
  31. Teye E, Uhomoibhi J, Wang H (2016) Nondestructive authentication of cocoa bean cultivars by FT-NIR spectroscopy and multivariate techniques. Focus Sci. CrossRefGoogle Scholar
  32. Veselá A, Barros AS, Synytsya A, Delgadillo I, Čopíková J, Coimbra MA (2007) Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder. Anal Chim Acta 601(1):77–86CrossRefPubMedGoogle Scholar
  33. Whitacre E, Oliver J, Van DBR, Van EP, Kremers B, Van DHB, Stewart M, Jansen-Beuvink A (2003) Predictive analysis of cocoa procyanidins using near-infrared spectroscopy techniques. J Food Sci 68:2618–2622CrossRefGoogle Scholar
  34. Windig W, Shaver J, Bro R (2008) Loopy MSC: a simple way to improve multiplicative scatter correction. J Appl Spectrosc 62(10):1153–1159CrossRefGoogle Scholar

Copyright information

© Association of Food Scientists & Technologists (India) 2018

Authors and Affiliations

  • Douglas Fernandes Barbin
    • 1
    Email author return OK on get
  • Leonardo Fonseca Maciel
    • 2
    • 3
  • Carlos Henrique Vidigal Bazoni
    • 3
  • Margareth da Silva Ribeiro
    • 2
  • Rosemary Duarte Sales Carvalho
    • 2
  • Eliete da Silva Bispo
    • 2
  • Maria da Pureza Spínola Miranda
    • 2
  • Elisa Yoko Hirooka
    • 3
  1. 1.Department of Food EngineeringUniversity of CampinasCampinasBrazil
  2. 2.College of PharmacyFederal University of BahiaSalvadorBrazil
  3. 3.Department of Food Science and TechnologyState University of LondrinaLondrinaBrazil

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