Case study of modeling covariance between external factors and sensory perception of coffee




Analysis and inference of sensory perceptions in coffee beverages are complex due to numerous random causes intrinsic to productivity, preparation, and especially consumer and/or taster subjectivity. In this context, latent variables often composed of a combination of other observed variables are discarded from conventional analyses. Following this argument, this study aimed to propose a model of structural equations applied to a database, geographical indication of coffees in Serra da Mantiqueira, with a methodological contribution characterized by inclusion of a treatment effect, contemplated by different altitudes at which coffees were produced. From the methodology used, a covariance structure was estimated, and used in another statistical methodology to discriminate the effects. It is concluded that the proposed model proved to be advantageous for allowing the analysis of the relationship of latent variables, production and environmental variations, which are not considered in a sensorial analysis, and showed that, in fact, they influence the sensorial perception, for the coffees produced in the Serra da Mantiqueira region. The correlation structure generated from the covariance matrix adjusted by the model resulted in estimates that could be used in other statistical methodologies more appropriate to discriminate the effects, exemplifying the use of principal components.

Key words: Latent variable; adjusted goodness-of-fit (AGFI); altitude; goodness-of-fit (GFI).


BARBOSA, J. N. et al. Coffee quality and its interactions with environmental factors in Minas Gerais, Brazil. Journal of Agricultural Science, 4(5):181-190, 2012.

BOLLEN, K. A.; NOBLE, M. D. Structural equation models and the quantification of behavior. PNAS, 108(3):15639-15646, 2011.

BORÉM, F. M. et al. Coffee sensory quality study based on spatial distribution in the Mantiqueira mountain region of Brazil. Journal of Sensory Studies, 35(2):e12552, 2020.

CÓRDOBA, N. et al. Specialty and regular coffee bean quality for cold and hot brewing: Evaluation of sensory profile and physicochemical characteristics. LWT, 145:111363, 2021.

ELHALIS, E.; COX, J.; ZHAO, J. Coffee fermentation: Expedition from traditional to controlled process and perspectives for industrialization. Applied Food Research, 3:100253, 2023.

HAMPTON, C. Estimating and reporting structural equation models with behavioral accounting data. Behavioral Research in Accounting, 27(2):1-34, 2015.

JÖRESKOG, K. G.; SORBOM, D. Lisrel VI: Analysisof linear structural relationships by maximum likehood and least square methods. Michigan: Scientific Software, 1986. 275p.

KONISHI, T. Principal component analysis for designed experiments. BMC Bioinformatics, 16(18):9-11, 2015.

MALTA, M. R. et al. Alterações na qualidade do café submetido a diferentes formas de processamento. Engenharia na Agricultura, 21(5):431-440, 2013.

McDONALD, R. P.; HARTMANN, W. M. A procedure for obtaining initial values of parameters in the RAM model. Multivariate Behavioral Research, 27(1):57-76, 1992.

QUINTÃO, R. T.; BRITO, E. P. Z. Connoisseurship consumption and market evolution: An institutional theory perspective on the growth of specialty coffee consumption in the USA. Brazilian Journal of Marketing, 15(1):1-15, 2016.

QUINTÃO, R. T.; BRITO, E. P. Z.; BELK, R. W. Connoisseurship consumption community and its dynamics. Review of Business Management, 19(63):1-17, 2017a.

QUINTÃO, R. T.; BRITO, E. P. Z.; BELK, R. W. The taste transformation ritual in the specialty coffee market. Revista de Administração De Empresas, 57(5):483-494, 2017b.

R DEVELOPMENT CORE TEAM. R: A language and environment for statistical computing. Vienna, 2021. 409p.

RAMOS, M. F. et al. Discrimination of the sensory quality of the Coffea arabica L. (cv. Yellow Bourbon) produced in different altitudes using decision trees obtained by the CHAID method. Journal of the science of food and agriculture, 96(10):3543-3551, 2016.

ROSSEEL Y. Lavaan: An R Package for structural equation modeling. Journal of Statistical Software, 48(2):1-36, 2012.

SANTOS, P. M. dos.; CIRILLO, M. Â.; GUIMARÃES, E. R. Specialty coffee in Brazil: Transition among consumers’ constructs using structural equation modeling. British Food Journal, 123(5):1-15, 2021.

SOCIEDADE NACIONAL DE AGRICULTURA - SNA. Consumo Brasileiro de café, cresce 3,5%, revela pesquisa da ABIC. 2018. Available in: <>. Access in: April 05, 2022.

SPECIALTY COFFEE ASSOCIATION OF AMERICA - SCAA. Protocolo para análise sensorial de café: Metodologia SCAA. [S.l.], 2008. Available in: < >. Access in: April 02, 2022.

WELDEMICHAEL, G.; TEFERI, D. The impact of climate change on coffee (Coffea Arabica L.). International Journal of Research Studies in Agricultural Sciences (IJRSAS), 5(11):26-34, 2020.




How to Cite

RESENDE, M.; BORÉM, F. M.; CIRILLO, M. Ângelo. Case study of modeling covariance between external factors and sensory perception of coffee. Coffee Science - ISSN 1984-3909, [S. l.], v. 18, p. e182112, 2023. DOI: 10.25186/.v18i.2112. Disponível em: Acesso em: 30 sep. 2023.