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Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo

Author

Listed:
  • Eugenio Ivorra

    (Institute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, Valencia, Spain)

  • Juan Camilo Sarria-González

    (Department of Agricultural Engineering, Surcolombian University, Neiva, Colombia)

  • Joel Girón-Hernández

    (Department of Agricultural Engineering, Surcolombian University, Neiva, Colombia)

Abstract

Artificial vision has wide-ranging applications in the food sector; it is easy to use, relatively low cost and allows to conduct rapid non-destructive analyses. The aim of this study was to use artificial vision techniques to control and model the coffee roasting process. Samples of Castillo variety coffee were used to construct the roasting curve, with captured images at different times. Physico-chemical determinations, such as colour, titratable acidity, pH, humidity and chlorogenic acids, and caffeine content, were investigated on the coffee beans. Data were processed by (i) Principal component analysis (PCA) to observe the aggrupation depending on the roasting time, and (ii) partial least squares (PLS) regression to correlate the values of the analytical determinations with the image information. The results allowed to construct robust regression models, where the colour coordinates (L*, a*), pH and titratable acidity presented excellent values in prediction (R2Pred 0.95, 0.91, 0.94 and 0.92). The proposed algorithms were capable to correlate the chemical composition of the beans at each roasting time with changes in the images, showing promising results in the modelling of the coffee roasting process.

Suggested Citation

  • Eugenio Ivorra & Juan Camilo Sarria-González & Joel Girón-Hernández, 2020. "Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 38(6), pages 388-396.
    • Handle: RePEc:caa:jnlcjf:v:38:y:2020:i:6:id:346-2019-cjfs
    DOI: 10.17221/346/2019-CJFS
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    References listed on IDEAS

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    1. Gabriel-Guzmán, Mauricio & Rivera, Victor M. & Cocotle-Ronzón, Yolanda & García-Díaz, Samuel & Hernandez-Martinez, Eliseo, 2017. "Fractality in coffee bean surface for roasting process," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 79-84.
    2. Barjolle, Dominique & Quiñones-Ruiz, Xiomara F. & Bagal, Monique & Comoé, Hermann, 2017. "The Role of the State for Geographical Indications of Coffee: Case Studies from Colombia and Kenya," World Development, Elsevier, vol. 98(C), pages 105-119.
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