Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo
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...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2020-12-01
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| Series: | Czech Journal of Food Sciences |
| Subjects: | |
| Online Access: | https://cjfs.agriculturejournals.cz/artkey/cjf-202006-0006_computer-vision-techniques-for-modelling-the-roasting-process-of-coffee-coffea-arabica-l-var-castillo.php |
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| author | Eugenio Ivorra Juan Camilo Sarria-González Joel Girón-Hernández |
| author_facet | Eugenio Ivorra Juan Camilo Sarria-González Joel Girón-Hernández |
| author_sort | Eugenio Ivorra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T08:31:49Z |
| format | Article |
| id | doaj.art-a0838550ea74425eb3d3cbdf4bf6fe7c |
| institution | Directory Open Access Journal |
| issn | 1212-1800 1805-9317 |
| language | English |
| last_indexed | 2024-04-10T08:31:49Z |
| publishDate | 2020-12-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Czech Journal of Food Sciences |
| spelling | doaj.art-a0838550ea74425eb3d3cbdf4bf6fe7c2023-02-23T03:28:41ZengCzech Academy of Agricultural SciencesCzech Journal of Food Sciences1212-18001805-93172020-12-0138638839610.17221/346/2019-CJFScjf-202006-0006Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. CastilloEugenio Ivorra0Juan Camilo Sarria-González1Joel Girón-Hernández2Institute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, Valencia, SpainDepartment of Agricultural Engineering, Surcolombian University, Neiva, ColombiaDepartment of Agricultural Engineering, Surcolombian University, Neiva, ColombiaArtificial 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.https://cjfs.agriculturejournals.cz/artkey/cjf-202006-0006_computer-vision-techniques-for-modelling-the-roasting-process-of-coffee-coffea-arabica-l-var-castillo.phpcolombian coffeevisible spectrumimage processingchemical composition |
| spellingShingle | Eugenio Ivorra Juan Camilo Sarria-González Joel Girón-Hernández Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo Czech Journal of Food Sciences colombian coffee visible spectrum image processing chemical composition |
| title | Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo |
| title_full | Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo |
| title_fullStr | Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo |
| title_full_unstemmed | Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo |
| title_short | Computer vision techniques for modelling the roasting process of coffee (Coffea arabica L.) var. Castillo |
| title_sort | computer vision techniques for modelling the roasting process of coffee coffea arabica l var castillo |
| topic | colombian coffee visible spectrum image processing chemical composition |
| url | https://cjfs.agriculturejournals.cz/artkey/cjf-202006-0006_computer-vision-techniques-for-modelling-the-roasting-process-of-coffee-coffea-arabica-l-var-castillo.php |
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