Impact of mass-to-volume ratios on beverage quality in coffee blends
DOI:
https://doi.org/10.25186/.v20i.2383Abstract
The discrimination of sensory attributes in coffee blends is complex due to the subjective nature of panelist evaluations and the intrinsic chemical heterogeneity of each variety. This study focuses on the influence of blend composition on the sensory evaluation of Robusta coffee, particularly its bitterness and astringency, depending on the proportion used. For specialty and commercial arabica coffees, positive attributes such as flavor and appearance are emphasized. The study also considers the impact of preparation concentrations, specifically the ratio of ground coffee (in grams) to water (in milliliters). We investigate coffee blends composed of different proportions of commercial roasted and ground Arabica coffee (CAC), Robusta coffee (RC), and a genotype of the Arabica specialty coffee (ASC) of the cultivar Yellow Bourbon, processed by both dry and wet methods. The blends were prepared at two concentrations: 7% (70 g ground coffee per 1,000 mL of water) and 10% (100 g ground coffee per 1,000 mL of water). Through predictive statistical
modeling, this research investigates the sensory outcomes of coffee blends, considering implications previously associated with chemical variables in the literature. It was found that blends prepared at 10% concentration (m/v) with intermediate proportions of CAC and RC achieved a balance in bitterness and flavor attributes for both dry and wet processed Yellow Bourbon. Including brew concentration in the models improved predictive power substantially, increasing R² from approximately 75% to over 90%. Therefore, this concentration can be recommended to ensure a balanced sensory profile without disproportionate influence from any single attribute.
Key words: Arabica coffee; robusta coffee; sensory analysis; mixture models; simplex regression.
References
ADHIKARI, J.; CHAMBERS, E.; KOPPEL, K. Impact of consumption temperature on sensory properties of hot brewed coffee. Food Research International, 115:95-104, 2018.
AGNOLETTI, B. Z. et al. Effect of fermentation on the quality of conilon coffee (Coffeacanephora): Chemical and sensory aspects. Microchemical Journal, 182:107966, 2022.
AKIYAMA, M. et al. Characterization of flavor compounds released during grinding of roasted robusta coffee beans.Food Science Technology Research, 11(3):298-307, 2005.
ASWATHI, K. N. et al. Functioning of Saccharomyces cerevisiae in honey coffee (Coffeacanephora) and their effect on metabolites, volatiles and flavor profiles. Food Research International, 180:114092, 2024.
BAKURADZE, T. et al. Four-week coffee consumption affects energy intake, satiety regulation, body fat, and protects DNA integrity. Food Research International, 63:420-427, 2014.
BELLO, L. H. A. D.; VIEIRA, A. F. C. Tutorial for mixture-process experiments with an industrial application. Pesquisa Operacional, 31(3):543-564, 2011.
BHUMIRATANA, N.; ADHIKARI, K.; CHAMBERS,E. Evolution of sensory aroma attributes from coffee beans to brewed coffee. LWT - Food Science and Technology, 44(10):2185-2192, 2011.
BRESSANI, A. P. P. et al. Into the minds of coffee consumers: perception, preference, and impact of information in the sensory analysis of specialty coffee. Food Science and Technology, 41:667-675, 2021.
CHAMBERS, E. et al. Development of a “living” lexicon for descriptive sensory analysis of brewed coffee. Journal Sensory Studies, 31(6):465-480, 2016.
CHANG, Y.-T. et al. Prediction of specialty coffee flavors based on near-infrared spectra using machine- and deep-learning methods. Journal of the Science of Food and Agriculture, 101(11):4705-4714, 2021.
CHENG, B. et al. Influence of genotype and environment on coffee quality.Trends Food Science & Technology, 57(A):20-30, 2016.
CIRILLO, M. A. et al. Statistical procedure for the composition of a sensory panel of blends of coffee with different qualities using the distribution of the extremes of the highest scores. Acta Scientiarum. Agronomy, 41:e39323, 2019.
CORDOBA CASTRO, N. et al. Coffee extraction: A review of parameters and their influence on the physicochemical characteristics and flavour of coffee brews. Trends in Food Science & Technology, 96:45-60, 2020.
CORNELL, J. A. Experiments with mixtures: Designs, models and the analysis of a mixture data. New York, Wiley, 2002. 680p.
COSTEL, E. A comparison of sensory methods in quality control. Food Quality and Preference, 13(6):341-353, 2002.
CRISTOVAM, E. et al. Gender preference in hedonic ratings for espresso and espresso-milk coffees. Food Quality and Preference, 11(6):437-444, 2000.
ELIAS, A. M. T. et al. Utilização de planejamento fatorial para avaliação do impacto da torrefação no perfil físico-químico de blends de café. Investigação, Sociedade e Desenvolvimento, 11(11):e174111133245, 2022.
FERREIRA, H. A. et al. Selecting a probabilistic model applied to the sensory analysis of specialty coffees performed with consumer.IEEE Latin America Transactions, 14(3):1507-1512, 2016.
FILHO, T. L. et al. Perfil sensorial e aceitabilidade de bebidas de café tipo expresso preparadas a partir de blends de café arábica e conillon. Enciclopédia Biosfera, 7(12):1-17. 2011.
GEROMEL, C. Effects of shade on the development and sugar metabolism of coffee (Coffea arabica L.) fruits.Plant Physiology and Biochemistry, 46(5-6):569-579, 2008.
GIACALONE, B.; BREDIE, W. L. P.; FROST, M. B. “All-In-One Test” (AI1): A rapid and easily applicable approach to consumer product testing. Food Quality and Preference, 27(2):108-119, 2013.
HAMEED, A. et al. Farm to consumer: factors affecting the organoleptic characteristics of coffee. II: postharvest processing factors. Comprehensive Reviews in Food Science and Food Safety, 17(5):1184-1237, 2018.
HE, Y. et al. PredCoffee: A binary classification approach specifically for coffee odor. iScience, 27(6):110041, 2024.
HECIMOVIC, I. et al. Comparative study of polyphenols and caffeine in different coffee varieties affected by the degree of roasting. Food Chemistry,129(3):991-1000, 2011.
KNOPP, S.; BYTOF, G.; SELMAR, D. Influence of processing on the content of sugars in green Arabica coffee beans. European Food Research and Technology, 223(2):195-201, 2006.
LISKA, G. R. et al. Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis. Semina: CiênciasAgrárias, 36(6):3671-3680, 2015.
LISKA, G. R. et al. Machine learning based on extended generalized linear model applied in mixture experiments. Communications in Statistics - Simulation and Computation,51(5):2511-2525, 2019.
LEE, L.W. et al. Coffee fermentation and flavor - An intricate and delicate relationship. Food Chemistry, 185:182-191, 2015.
LOUZADA, L. P. et al. The consistency in the sensory analysis of coffees using Q-graders. European Food Research and Technology, 243:1545-1554, 2017.
MAHMUD, M. M. C.; SHELLIE, R. A.; KEAST, R. Unravelling the relationship between aroma compounds and consumer acceptance: Coffee as an example. Comprehensive Reviews in Food Science and Food Safety, 19(5):2380-2420, 2020.
MALAQUIAS, J. V.; CELESTINO, S. M. C.; XAVIER, M. F. F. Optimization of the roasting conditions of arabica coffee cultivated in the cerrado area of Brazil. Brazilian Journal Food Technology, 21:e2016162, 2018.
MCCAIN-KEEFER, H. R.; MEALS, S.; DRAKE, M. The sensory properties and consumer acceptance of cold brew coffee. Journal of Sensory Studies, 35(6):e12604, 2020.
OLIVEIRA, P. D. et al. Quality of natural and pulped coffee as a function of temperature changes during mechanical drying. Coffee Science, 13(4):415-425, 2018.
OLIVEIRA, G. H. H. O.; OLIVEIRA, A. P. L. R. Coffee roasting, blending, and grinding: nutritional, sensorial and sustainable aspects. Agriculture, 13(11):2116, 2023.
PACETTI, D. et al. Authentication of Italian espresso coffee blends through the GC peak ratio between kahweol and 16-O-methyl cafestol. Food Chemistry, 135(3):1569-1574, 2012.
PATERSON, R. R. M.; LIMA, N.; TANIWAKI, M. H. Coffee, mycotoxins and climate change. Food Research International, 61:1-15, 2014.
PIRES, F. de C. et al. Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction. Journal of The Science of Food and Agriculture, 104(9):5435-5441, 2024.
PRAKASH, I. et al. Metabolomics and volatile fingerprint of yeast fermented robusta coffee: A value added coffee. LWT, 154:112717, 2022.
R CORE TEAM. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2025. Available in: <https://www.R-project.org/>. Access in: October 21, 2025.
RAMOS, M. F. et al. Discrimination of the sensory quality of the Coffee 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.
RICHARD, D. S.; CHAMBERS, E. Coffee flavor: A Review. Beverages, 6(3):44, 2020.
SANTOS, E. S. M. et al. Effect conilon beans on the sensory profile and consumer acceptance of coffee beverages. Semina: Ciências Agrárias, 34(5):2297-2306, 2013.
SANTOS, W. W. V. et al. Influência das condições de torra e do processo extrativo em blends de café. Brazilian Journal of Development, 6(5):25079-25092, 2020.
SPECIALTY COFFEE ASSOCIATION - SCA. Coffee standards. 2021. Available in: https://sca.coffee/research/coffee-standards. Access in: October 21, 2025.
SHANKAR, S. R. et al. Microbial ecology and functional coffee fermentation dynamics with Pichia kudriavzevii. Food Microbiology, 105:104012, 2022.
SIRIDEVI, G. B. et al. Coffee starter microbiome and in-silico approach to improve Arabica coffee.LWT, 114:108382, 2019.
SKURICHINA, M.; DUIN, R. P. W. Boosting in linear discriminant analysis.Multiple classifier systems. In: Multiple Classifier Systems. MCS 2000. Lecture notes in computer science, p.190-199, 2000.
VELLEMA, W. et al. The effect of specialty coffee certification on household livelihood strategies and specialization. Food Policy, 57:13-25, 2015.
WINTGENS, J. N. Coffee: Growing, processing, sustainable production. A guidebook for growers, processors, traders and researchers, Wiley-VCH, 2012. 1040p.
WILLDEN, J. L. C. Mixture Experiments in R Using mixexp. Journal of Statistical Software, 72:1-20, 2016.
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