Multivariate analysis applied for correlations between analytical measures and sensory profile of goat milk chocolate

  • Grazielly de Jesus Silva
  • Ben-Hur Ramos Ferreira Gonçalves
  • Daniele Gomes Conceição
  • Gabrielle Cardoso Reis Fontan
  • Leandro Soares Santos
  • Sibelli Passini Barbosa FerrãoEmail author
Original Article


The aim of this work was to characterize goat milk chocolates with different concentrations of cocoa (35%, 45%, 55% and 65%) and apply correlations between sensory features and analytical measures. The chocolates were evaluated through moisture, ashes, fat content, protein, acidity, pH, water activity, texture, instrumental color and sensory profile. The correlations showed that the brown color can be represented by the chromaticity coordinates a* and b* and the flavor attributes (sweet taste and bitter taste), by the ashes analysis, fat content and pH. Canonic scores superior to 0.5 indicate chocolates with better acceptance.


Chemometry Chocolate Cocoa Goat Milk 



The authors acknowledge the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) for providing study scholarships and the CEPLAC for the contribution on the production of the chocolates.


  1. Afoakwa EO, Paterson A, Fowler M (2007) Factors influencing rheological and textural qualities in chocolate—a review. Trends Food Sci Technol 18:290–298. CrossRefGoogle Scholar
  2. Afoakwa EO, Paterson A, Fowler M, Ryan A (2008a) Flavor formation and character in cocoa and chocolate: a critical review. Food Sci Nutr 48:840–857. CrossRefGoogle Scholar
  3. Afoakwa EO, Paterson A, Fowler M, Vieira J (2008b) Particle size distribution and compositional effects on textural properties and appearance of dark chocolates. J Food Eng 87:181–190. CrossRefGoogle Scholar
  4. Afoakwa EO, Paterson A, Fowler M, Vieira J (2009) Microstructure and mechanical properties related to particle size distribution and composition in dark chocolate. J Food Eng 44:111–119. CrossRefGoogle Scholar
  5. Aidoo RP, De Clercq N, Afoakwa EO, Dewettinck K (2014) Optimisation of processing conditions and rheological properties using Stephan Mixer as conche in small-scale chocolate processing. Int J Food Sci Technol 49:740–746. CrossRefGoogle Scholar
  6. Bourne M (2002) Food texture and viscosity: concept and measurement. Academic Press, CambridgeCrossRefGoogle Scholar
  7. Brazil. Ministério da Saúde. Agência Nacional de Vigilância Sanitária. Resolução – RDC nº 12 (2001) Regulamento Técnico sobre padrões microbiológicos para alimentos. Diário Oficial da União, Brasília, DFGoogle Scholar
  8. Callegari-Jacques SM (2006) Bioestatística: princípios e aplicações. Artmed, Porto AlegreGoogle Scholar
  9. Carvalho FIF (2004) Estimativas e implicações da correlação no melhoramento vegetal. Pelotas, Ed. Universitária da UFPelGoogle Scholar
  10. Chaves JBP, Sproesser RL (2005) Práticas de Laboratório de Análise Sensorial de Alimentos e Bebidas. Editora da Universidade Federal de Viçosa, ViçosaGoogle Scholar
  11. CIE Commission Internationale de L’Éclairage (2004) Colorimetry. CIE Publication, ViennaGoogle Scholar
  12. Do TAL, Hargreaves JM, Wolf B, Hort J, Mitchell JR (2007) Impact of particle size distribution on rheological and textural properties of chocolate models with reduced fat content. J Food Sci 72:541–552. CrossRefGoogle Scholar
  13. Efraim P, Pezoa-García NH, Jardim DCP, Nishikawa A, Haddad R, Eberlin MN (2010) Influence of cocoa beans fermentation and drying on the polyphenol content and sensory acceptance. Food Sci Technol 30:142–150. CrossRefGoogle Scholar
  14. FDA Food and Drug Administration (2005) Bacteriological analytical. Accessed 28 July 2016
  15. Ferreira DF (2011) Estatística Multivariada. Lavras, Editora UFLAGoogle Scholar
  16. Glicerina V, Balestra F, Rosa MD, Romani S (2016) Microstructural and rheological characteristics of dark, milk and white chocolate: a comparative study. J Food Eng 169:165–171. CrossRefGoogle Scholar
  17. IAL Instituto Adolfo Lutz (2008) Métodos físico-químicos para análise de alimentos Brasília, Ministério da SaúdeGoogle Scholar
  18. Johnson RA, Wichern DW (1999) Applied multivariate statistical analysis. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  19. Jolliffe IT (1972) Discarding variables in a principal component analysis. I: artificial data. J R Stat Soc 21:160–173. CrossRefGoogle Scholar
  20. Larrigaudiere C, Lentheric I, Puy J, Pinto E (2004) Biochemical characterization of core browning and brown heart disorders in pear by multivariate analysis. Postharvest Biol Technol 31:29–39. CrossRefGoogle Scholar
  21. Lawless HT, Heymann H (2010) Sensory evaluation of food: principles and practices. Springer, New YorkCrossRefGoogle Scholar
  22. Massart DL, Vandeginste BGM, Buydens LMC, JongS Lewi PJ, Smeyers-Berbeke J (1998) Handbook of chemometrics and qualimetrics: part A. Elsevier Science B. V., AmsterdamGoogle Scholar
  23. Mingoti SA (2005) Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Belo Horizonte, Editora UFMGGoogle Scholar
  24. Morrison DF (1978) Multivariate statistical methods. Tokyo, Mc Graw HillGoogle Scholar
  25. Moskowitz HR (1983) Product testing and sensory evaluation of foods. Food and Nutrition Press, WestportGoogle Scholar
  26. Richter VB, Almeida TCA, Prudencio SH, Benassi MT (2010) Proposing a ranking descriptive sensory method. Food Qual Prefer 21:611–620. CrossRefGoogle Scholar
  27. Silva RCSN, Minim VPR, Carneiro JDS, Nascimento M, Lucia SMD, Minim LA (2013) Quantitative sensory description using the optimized descriptive profile: comparison with conventional and alternative methods for evaluation of chocolate. Food Qual Prefer 30:169–179. CrossRefGoogle Scholar
  28. Steel RGD, Torrie JH, Dickey DA (1997) Principles and procedures of statistics: a biometrical approach. McGraw Hill Book, New YorkGoogle Scholar
  29. Stone H, Sidel JL, Oliver S, Wooley A, Sinon RC (1974) Sensory evaluation by quantitative descriptive analysis. Food Technol 28:24–34. CrossRefGoogle Scholar
  30. Szczesniak AS (2002) Texture is a sensory property. Food Qual Prefer 13:215–225. CrossRefGoogle Scholar

Copyright information

© Association of Food Scientists & Technologists (India) 2019

Authors and Affiliations

  • Grazielly de Jesus Silva
    • 1
  • Ben-Hur Ramos Ferreira Gonçalves
    • 1
  • Daniele Gomes Conceição
    • 1
  • Gabrielle Cardoso Reis Fontan
    • 1
  • Leandro Soares Santos
    • 1
  • Sibelli Passini Barbosa Ferrão
    • 1
    Email author
  1. 1.Program in Food Engineering and ScienceUniversidade Estadual do Sudoeste da Bahia (UESB)ItapetingaBrazil

Personalised recommendations