A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics
This study seeks to determine the influence of process variables: consumption percentage in the mixture, pasilla percentage in the mixture, storage time, humidity percentage in the product for consumption, humidity percentage in the pasilla, humidity percentage in roasted coffee, average humidity in...
- Autores:
-
Parody Muñoz, Alexander Elias
Charris, Dhizzy
amelec, viloria
Cervera Cárdenas, Jorge Eduardo
Hernandez, Hugo
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8019
- Acceso en línea:
- https://hdl.handle.net/11323/8019
https://doi.org/10.1007/978-981-15-5558-9_12
https://repositorio.cuc.edu.co/
- Palabra clave:
- Multiple linear regression
Shrinkage in a process
Humidity
Statistical quality control
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| title |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| spellingShingle |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics Multiple linear regression Shrinkage in a process Humidity Statistical quality control |
| title_short |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| title_full |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| title_fullStr |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| title_full_unstemmed |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| title_sort |
A method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics |
| dc.creator.fl_str_mv |
Parody Muñoz, Alexander Elias Charris, Dhizzy amelec, viloria Cervera Cárdenas, Jorge Eduardo Hernandez, Hugo |
| dc.contributor.author.spa.fl_str_mv |
Parody Muñoz, Alexander Elias Charris, Dhizzy amelec, viloria Cervera Cárdenas, Jorge Eduardo Hernandez, Hugo |
| dc.subject.spa.fl_str_mv |
Multiple linear regression Shrinkage in a process Humidity Statistical quality control |
| topic |
Multiple linear regression Shrinkage in a process Humidity Statistical quality control |
| description |
This study seeks to determine the influence of process variables: consumption percentage in the mixture, pasilla percentage in the mixture, storage time, humidity percentage in the product for consumption, humidity percentage in the pasilla, humidity percentage in roasted coffee, average humidity in finished product, average color in roasted coffee, and average color in finished product, for the shrinkage of packed coffee in a coffee processing plant of Arabica type. Using a multiple linear regression model, the study stated that the variables of humidity percentage of roasted coffee and color of roasted coffee have a statistically significant relationship with a confidence of 95% (p-value < 0.05). It was concluded that these variables explain 99.95% of the variability in the shrinkage, and the relation of the shrinkage with the humidity percentage is inversely proportional, but the relation of this variable with the color of roasted coffee is directly proportional. The tests applied to the model wastes proved that the model is suitable for predicting the shrinkage in the process. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-09-08 |
| dc.date.accessioned.none.fl_str_mv |
2021-03-15T20:39:38Z |
| dc.date.available.none.fl_str_mv |
2021-03-15T20:39:38Z |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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18761119 18761100 |
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https://hdl.handle.net/11323/8019 |
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https://doi.org/10.1007/978-981-15-5558-9_12 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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18761119 18761100 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/8019 https://doi.org/10.1007/978-981-15-5558-9_12 https://repositorio.cuc.edu.co/ |
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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
1. Moscoso M (2016) El café, una de las bebidas más consumidas del mundo.[Online] natural medio ambiente. Obtenido de: https://www.natura-medioambiental.com/el-cafe-una-de-las-bebidas-mas-consumidas-del-mundo/. Acceso 5 abril 2018 2. Nuñez J (2002) Optimización de la Producción en la Empresa Elaborados de Café 3. Suarez H, Bello H (2016) Estudio de viabilidad para la modernización del proceso de tostión de una de las líneas de café tostado y molido de la empresa Café de Colombia 4. Parody A et al (2018) Application of a central design composed of surface of response for the determination of the flatness in the steel sheets of a colombian steel. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 5. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham 6. Viloria A et al (2018) Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 7. Parody A, Viloria A, Lis JP, Malagón LE, Calí EG, Hernández Palma H (2018) Application of an experimental design of D-optimum mixing based on restrictions for the optimization of the pre-painted steel line of a steel producer and marketing company. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham 8. Castaño J, Quintero G (2001) Optimización de la torrefacción de mezclas de café sano y brocado, en función de la temperatura de proceso y el agua de apagado 9. Kizys R, Juan A (2005) Modelo de regresión lineal múltiple 10. Conejo AJ, Contreras J, Espinola R, Plazas MA (2005) Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecast 21(3):435–462 11. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297 12. Du XF, Leung SCH, Zhang JL, Lai KK (2011) Demand forecasting of perishable farm products using support vector machine. Int J Syst Sci 44(3):556–567 13. Garcia MI (2003) Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales. Universitas Scientiarum 8:45–50 14. Garson GD (1991) Interpreting neural network connection weights. AI Expert, pp 47–51 15. Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8(2):209–218 16. Glorfeld LW (1996) A methodology for simplification and interpretation of backpropagation-based neural network models. Expert Syst Appl 10(1):37–54 17. Gunn SR (1998) Support vector machines for classification and regression. ISIS 14(1): 5–16 18. Hanke JE, Wichern DW (2006) Pronósticos en los negocios. Pearson Educación 19. Heravi S, Osborn DR, Birchenhall CR (2004) Linear versus neural network forecasts for European industrial production series. Int J Forecast 20(3):435–446 20. Izar J, Ynzunza C, Guarneros O (2016) Variabilidad de la demanda del tiempo de entrega, existencias de seguridad y costo del inventario Contaduría y Administración 61(3): 499–513 |
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Parody Muñoz, Alexander EliasCharris, Dhizzyamelec, viloriaCervera Cárdenas, Jorge EduardoHernandez, Hugo2021-03-15T20:39:38Z2021-03-15T20:39:38Z2020-09-081876111918761100https://hdl.handle.net/11323/8019https://doi.org/10.1007/978-981-15-5558-9_12Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This study seeks to determine the influence of process variables: consumption percentage in the mixture, pasilla percentage in the mixture, storage time, humidity percentage in the product for consumption, humidity percentage in the pasilla, humidity percentage in roasted coffee, average humidity in finished product, average color in roasted coffee, and average color in finished product, for the shrinkage of packed coffee in a coffee processing plant of Arabica type. Using a multiple linear regression model, the study stated that the variables of humidity percentage of roasted coffee and color of roasted coffee have a statistically significant relationship with a confidence of 95% (p-value < 0.05). It was concluded that these variables explain 99.95% of the variability in the shrinkage, and the relation of the shrinkage with the humidity percentage is inversely proportional, but the relation of this variable with the color of roasted coffee is directly proportional. The tests applied to the model wastes proved that the model is suitable for predicting the shrinkage in the process.Parody Muñoz, Alexander Elias-will be generated-orcid-0000-0003-0155-266X-600Charris, Dhizzyamelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Cervera Cárdenas, Jorge Eduardo-will be generated-orcid-0000-0002-8791-6630-600Hernandez, Hugo-will be generated-orcid-0000-0002-7634-7161-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007/978-981-15-5558-9_12Multiple linear regressionShrinkage in a processHumidityStatistical quality controlA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statisticsPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. Moscoso M (2016) El café, una de las bebidas más consumidas del mundo.[Online] natural medio ambiente. Obtenido de: https://www.natura-medioambiental.com/el-cafe-una-de-las-bebidas-mas-consumidas-del-mundo/. Acceso 5 abril 20182. Nuñez J (2002) Optimización de la Producción en la Empresa Elaborados de Café3. Suarez H, Bello H (2016) Estudio de viabilidad para la modernización del proceso de tostión de una de las líneas de café tostado y molido de la empresa Café de Colombia4. Parody A et al (2018) Application of a central design composed of surface of response for the determination of the flatness in the steel sheets of a colombian steel. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham5. Izquierdo NV, Lezama OBP, Dorta RG, Viloria A, Deras I, Hernández-Fernández L (2018) Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham6. Viloria A et al (2018) Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham7. Parody A, Viloria A, Lis JP, Malagón LE, Calí EG, Hernández Palma H (2018) Application of an experimental design of D-optimum mixing based on restrictions for the optimization of the pre-painted steel line of a steel producer and marketing company. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham8. Castaño J, Quintero G (2001) Optimización de la torrefacción de mezclas de café sano y brocado, en función de la temperatura de proceso y el agua de apagado9. Kizys R, Juan A (2005) Modelo de regresión lineal múltiple10. Conejo AJ, Contreras J, Espinola R, Plazas MA (2005) Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecast 21(3):435–46211. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–29712. Du XF, Leung SCH, Zhang JL, Lai KK (2011) Demand forecasting of perishable farm products using support vector machine. Int J Syst Sci 44(3):556–56713. Garcia MI (2003) Análisis Y Predicción De La Serie De Tiempo Del Precio Externo Del Café Colombiano Utilizando Redes Neuronales Artificiales. Universitas Scientiarum 8:45–5014. Garson GD (1991) Interpreting neural network connection weights. AI Expert, pp 47–5115. Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8(2):209–21816. Glorfeld LW (1996) A methodology for simplification and interpretation of backpropagation-based neural network models. Expert Syst Appl 10(1):37–5417. Gunn SR (1998) Support vector machines for classification and regression. ISIS 14(1): 5–1618. Hanke JE, Wichern DW (2006) Pronósticos en los negocios. Pearson Educación19. Heravi S, Osborn DR, Birchenhall CR (2004) Linear versus neural network forecasts for European industrial production series. Int J Forecast 20(3):435–44620. Izar J, Ynzunza C, Guarneros O (2016) Variabilidad de la demanda del tiempo de entrega, existencias de seguridad y costo del inventario Contaduría y Administración 61(3): 499–513PublicationORIGINALA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdfA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdfapplication/pdf142736https://repositorio.cuc.edu.co/bitstreams/e9c48768-3d66-416e-8899-a5f8cdfaeb63/downloadec3917fa5f150391ea50a6dcccd156d1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/612900c7-d6b0-40e2-9742-d63bba78360c/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/31e2499f-7448-4db4-bf1c-8d4f2724cb44/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdf.jpgA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdf.jpgimage/jpeg40005https://repositorio.cuc.edu.co/bitstreams/3f933363-391b-4fc2-afd9-38db13e7795d/downloadaf105e6dd2037c4a0bfc5de2b420bd60MD54TEXTA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdf.txtA method for the prediction of the shrinkage in roasted and ground coffee using multivariable statistics.pdf.txttext/plain1440https://repositorio.cuc.edu.co/bitstreams/4f0f92d1-99ed-4a8d-bc8b-308a593a6715/download5f46acd8e5fecc618b656502aaf09c77MD5511323/8019oai:repositorio.cuc.edu.co:11323/80192024-09-17 12:48:21.944http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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