Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed
•Coffee color during roasting in a spouted bed was assessed by an ANFIS model.•The input variables were color parameters obtained from digital images.•For monitoring in the process line, we constructed an image-capture device.•The model's goodness of fit was assessed by R2, RMSE and SRIC.•The m...
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| Published in | Expert systems with applications Vol. 54; pp. 162 - 169 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.07.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2016.01.027 |
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| Abstract | •Coffee color during roasting in a spouted bed was assessed by an ANFIS model.•The input variables were color parameters obtained from digital images.•For monitoring in the process line, we constructed an image-capture device.•The model's goodness of fit was assessed by R2, RMSE and SRIC.•The model is proper for monitoring in the process line.
An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L*), browning index (BI) and the distance to a defined standard (ΔE). An image-capture device was designed to monitor color variations in the L*a*b* space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500°C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error <0.002 and a modified Schwarz–Rissanen information criterion <0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process. |
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| AbstractList | An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L super(*)), browning index (BI) and the distance to a defined standard ( Delta E). An image-capture device was designed to monitor color variations in the L super(*)a super(*)b super(*) space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500 degree C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error < 0.002 and a modified Schwarz-Rissanen information criterion < 0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process. •Coffee color during roasting in a spouted bed was assessed by an ANFIS model.•The input variables were color parameters obtained from digital images.•For monitoring in the process line, we constructed an image-capture device.•The model's goodness of fit was assessed by R2, RMSE and SRIC.•The model is proper for monitoring in the process line. An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L*), browning index (BI) and the distance to a defined standard (ΔE). An image-capture device was designed to monitor color variations in the L*a*b* space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500°C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error <0.002 and a modified Schwarz–Rissanen information criterion <0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process. |
| Author | Arriola-Guevara, Enrique Guatemala-Morales, Guadalupe M. Virgen-Navarro, Luis Corona-González, Rosa I. Herrera-López, Enrique J. |
| Author_xml | – sequence: 1 givenname: Luis surname: Virgen-Navarro fullname: Virgen-Navarro, Luis email: luisvirgen83@outlook.com organization: Unidad de Tecnología Alimentaria, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Av. Normalistas No. 800, Guadalajara, Jalisco 44270, Mexico – sequence: 2 givenname: Enrique J. surname: Herrera-López fullname: Herrera-López, Enrique J. email: eherrera@ciatej.mx organization: Unidad de Biotecnología Industrial, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Av. Normalistas No. 800, Guadalajara, Jalisco 44270, Mexico – sequence: 3 givenname: Rosa I. surname: Corona-González fullname: Corona-González, Rosa I. email: rcoronagonzalez1@gmail.com organization: Departamento de Ingeniería Química, Universidad de Guadalajara, Blvd. Marcelino García Barragán No. 1412, Guadalajara, Jalisco 44230, Mexico – sequence: 4 givenname: Enrique surname: Arriola-Guevara fullname: Arriola-Guevara, Enrique email: arriole@hotmail.com organization: Departamento de Ingeniería Química, Universidad de Guadalajara, Blvd. Marcelino García Barragán No. 1412, Guadalajara, Jalisco 44230, Mexico – sequence: 5 givenname: Guadalupe M. orcidid: 0000-0003-1452-7738 surname: Guatemala-Morales fullname: Guatemala-Morales, Guadalupe M. email: guadisga@msn.com organization: Unidad de Tecnología Alimentaria, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Av. Normalistas No. 800, Guadalajara, Jalisco 44270, Mexico |
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| Keywords | Coffee roasting Neuro-fuzzy model Digital image Spouted bed Moisture content Color |
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| SubjectTerms | Artificial neural networks Beans Coffee Coffee roasting Color Digital image Fuzzy logic Fuzzy set theory Mathematical models Moisture content Monitoring Neuro-fuzzy model Roasting Spouted bed |
| Title | Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed |
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