Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks
Modelling a process requires a priori knowledge of the possible causal relationships between the study variables and the response, especially when the raw material is waste. A new approach to characterizing a manufacturing process is presented to extend data that are difficult to obtain by direct ex...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10857333/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857142422044672 |
|---|---|
| author | Rosa Trasvina-Osorio Sergio Alonso-Romero Juan de Anda-Suarez Valentin Calzada-Ledesma Javier Yanez-Mendiola Luis Fernando Villanueva-Jimenez Erick Rojas-Mancera |
| author_facet | Rosa Trasvina-Osorio Sergio Alonso-Romero Juan de Anda-Suarez Valentin Calzada-Ledesma Javier Yanez-Mendiola Luis Fernando Villanueva-Jimenez Erick Rojas-Mancera |
| author_sort | Rosa Trasvina-Osorio |
| collection | DOAJ |
| description | Modelling a process requires a priori knowledge of the possible causal relationships between the study variables and the response, especially when the raw material is waste. A new approach to characterizing a manufacturing process is presented to extend data that are difficult to obtain by direct experimentation. This research aims to identify industrial applications for derivatives from animal by-products, such as tilapia skin, to develop an alternative methodology for issues related to collagen. Empirical data from a design of experiments (DoE) fed a Markov chain Monte Carlo algorithm in an artificial neural network (MCMC-ANN) for data imputation, numerical simulation, and training. The DoE model presented a coefficient of 86.92% in the observed data in the first stage of the methodology. In the second stage, the predictive model MCMC-ANN presented a mean square error (MSE) of 0.00171, and a standard deviation (SD) of 0.00133. The model was tested using validation data obtained outside the training process. The SD of empirical validation of MCMC-ANN were 1.7890, 0.2051 and 0.0919 for three random points. The product of this research is a four-dimensional predictive model that estimates the yield with decreasing material, energy resources, and the time required for the collagen extraction process from the skin waste of tilapia. The proposed tool obtains a minimum adjustment with a deviation whose tolerance is valid for predicting the collagen extraction yield. MCMC-ANN can be applied to any case that requires a deeper analysis with more data or experiments. |
| format | Article |
| id | doaj-art-85ccad2396f34748afcfc606db0206ee |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-85ccad2396f34748afcfc606db0206ee2025-02-12T00:02:33ZengIEEEIEEE Access2169-35362025-01-0113256122562110.1109/ACCESS.2025.353632710857333Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural NetworksRosa Trasvina-Osorio0https://orcid.org/0000-0003-0895-2009Sergio Alonso-Romero1https://orcid.org/0000-0001-6469-0408Juan de Anda-Suarez2https://orcid.org/0000-0003-3728-0459Valentin Calzada-Ledesma3Javier Yanez-Mendiola4Luis Fernando Villanueva-Jimenez5Erick Rojas-Mancera6Centro de Innovación Aplicada en Tecnologías Competitivas, León, Guanajuato, MexicoCentro de Innovación Aplicada en Tecnologías Competitivas, León, Guanajuato, MexicoInstituto Tecnológico Superior de Purísima del Rincón, Tecnológico Nacional de México, Purísima del Rincón, Guanajuato, MexicoInstituto Tecnológico Superior de Purísima del Rincón, Tecnológico Nacional de México, Purísima del Rincón, Guanajuato, MexicoCentro de Innovación Aplicada en Tecnologías Competitivas, León, Guanajuato, MexicoInstituto Tecnológico Superior de Purísima del Rincón, Tecnológico Nacional de México, Purísima del Rincón, Guanajuato, MexicoInstituto Tecnológico Superior de Purísima del Rincón, Tecnológico Nacional de México, Purísima del Rincón, Guanajuato, MexicoModelling a process requires a priori knowledge of the possible causal relationships between the study variables and the response, especially when the raw material is waste. A new approach to characterizing a manufacturing process is presented to extend data that are difficult to obtain by direct experimentation. This research aims to identify industrial applications for derivatives from animal by-products, such as tilapia skin, to develop an alternative methodology for issues related to collagen. Empirical data from a design of experiments (DoE) fed a Markov chain Monte Carlo algorithm in an artificial neural network (MCMC-ANN) for data imputation, numerical simulation, and training. The DoE model presented a coefficient of 86.92% in the observed data in the first stage of the methodology. In the second stage, the predictive model MCMC-ANN presented a mean square error (MSE) of 0.00171, and a standard deviation (SD) of 0.00133. The model was tested using validation data obtained outside the training process. The SD of empirical validation of MCMC-ANN were 1.7890, 0.2051 and 0.0919 for three random points. The product of this research is a four-dimensional predictive model that estimates the yield with decreasing material, energy resources, and the time required for the collagen extraction process from the skin waste of tilapia. The proposed tool obtains a minimum adjustment with a deviation whose tolerance is valid for predicting the collagen extraction yield. MCMC-ANN can be applied to any case that requires a deeper analysis with more data or experiments.https://ieeexplore.ieee.org/document/10857333/CollagentilapiaMarkov chain Monte Carloartificial neural network |
| spellingShingle | Rosa Trasvina-Osorio Sergio Alonso-Romero Juan de Anda-Suarez Valentin Calzada-Ledesma Javier Yanez-Mendiola Luis Fernando Villanueva-Jimenez Erick Rojas-Mancera Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks IEEE Access Collagen tilapia Markov chain Monte Carlo artificial neural network |
| title | Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks |
| title_full | Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks |
| title_fullStr | Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks |
| title_full_unstemmed | Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks |
| title_short | Characterization of the Collagen Extraction Manufacturing Process Using Markov Chains and Artificial Neural Networks |
| title_sort | characterization of the collagen extraction manufacturing process using markov chains and artificial neural networks |
| topic | Collagen tilapia Markov chain Monte Carlo artificial neural network |
| url | https://ieeexplore.ieee.org/document/10857333/ |
| work_keys_str_mv | AT rosatrasvinaosorio characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT sergioalonsoromero characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT juandeandasuarez characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT valentincalzadaledesma characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT javieryanezmendiola characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT luisfernandovillanuevajimenez characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks AT erickrojasmancera characterizationofthecollagenextractionmanufacturingprocessusingmarkovchainsandartificialneuralnetworks |