Research Article Open Access

Shelf-Life Prediction of Specialty Coffees using the Arrhenius Model

Frank Fernández Rosillo1, María Alina Cueva Ríos1, Lenin Quiñones Huatangari2 and Carla Guianella Samaniego Lalangui2
  • 1 Instituto de Ciencia y Tecnología de Alimentos, Departamento Académico de Industrias Alimentarias, Facultad de Ingeniería, Universidad Nacional de Jaén, Cajamarca, Peru
  • 2 Grupo de Modelamiento y Simulación de Procesos en la Industria Alimentaria, Instituto de Investigación de Ciencia de Datos, Universidad Nacional de Jaén, Cajamarca, Peru

Abstract

The study estimated the shelf life of roasted and ground specialty coffees in bi-laminated and tri-laminated packaging concerning cup score, using the Acceleration Test at temperatures of 40, 50, and 60°C. The sensory evaluation was carried out by Q Grader tasters. Based on the higher value of the coefficient of determination of the regressions resulting from the cup and temperature profiles, it was determined that the order of the reaction was of the first order, with a second regression adjusted to the Arrhenius equation. The activation energy and the pre-exponential factor were obtained, variables with which the degradation rate constant was determined for each package and temperature of study. Employing the first-order kinetic equation, the results of shelf life at storage temperatures for 10, 15, 20, 25, 30, 40, 50, and 60°C were estimated at 26.9, 21.9, 17.8, 14.7, 12.2, 8.5, 6.1 and 4.4 days for bi-laminated packaging and 137.9, 93.8, 64.6, 45.1, 31.8 16.4, 8.8 and 4.9 days for tri-laminated, using a good performance prediction technique to determine quality descriptors of specialty coffees.

OnLine Journal of Biological Sciences
Volume 23 No. 1, 2023, 17-24

DOI: https://doi.org/10.3844/ojbsci.2023.17.24

Submitted On: 1 October 2022 Published On: 23 December 2022

How to Cite: Rosillo, F. F., Ríos, M. A. C., Huatangari, L. Q. & Lalangui, C. G. S. (2023). Shelf-Life Prediction of Specialty Coffees using the Arrhenius Model. OnLine Journal of Biological Sciences, 23(1), 17-24. https://doi.org/10.3844/ojbsci.2023.17.24

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Keywords

  • Coffee Variety
  • Modelling
  • Prediction
  • Test Accelerated