Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms
This research aimed to automate the fitting of kinetic models using genetic algorithms (GAs) to optimize the estimation of kinetic parameters—the reaction rate constant (k) and the initial value of total titratable acidity (TTA, C₀)—and enhance predictive accuracy. Experimental data were collected...
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| Format: | Article |
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Universidad Autónoma de Chihuahua
2025-06-01
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| Series: | Tecnociencia Chihuahua |
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| Online Access: | https://revistascientificas.uach.mx/index.php/tecnociencia/article/view/1847 |
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| author | Alejandro Kevin Méndez Castillo Elizabeth Contreras López Jesús Guadalupe Pérez Flores Laura García Curiel Emmanuel Pérez Escalante Karla Soto Vega Carlos Ángel-Jijón Alicia Cervantes Elizarrarás |
| author_facet | Alejandro Kevin Méndez Castillo Elizabeth Contreras López Jesús Guadalupe Pérez Flores Laura García Curiel Emmanuel Pérez Escalante Karla Soto Vega Carlos Ángel-Jijón Alicia Cervantes Elizarrarás |
| author_sort | Alejandro Kevin Méndez Castillo |
| collection | DOAJ |
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This research aimed to automate the fitting of kinetic models using genetic algorithms (GAs) to optimize the estimation of kinetic parameters—the reaction rate constant (k) and the initial value of total titratable acidity (TTA, C₀)—and enhance predictive accuracy. Experimental data were collected from bananas (Musa paradisiaca L.) at ripening stage 2 on Von Loesecke scale, measuring TTA at specific intervals, and three kinetic models—zero-order, first-order, and second-order—were fitted. For optimization, Python-based GA was used to minimize the mean squared error (MSE) and evaluate performance based on R2, AIC, and BIC. Results indicated that while the zero-order model provided the best statistical fit (R2 = 0.918, lowest AIC and BIC), validation on unseen data favored the second-order model, which exhibited superior predictive accuracy (test R2 = 0.9349, lowest test MSE). The integration of GAs enhanced the robustness of kinetic fitting by effectively navigating parameter space and avoiding local minima, outperforming traditional optimization methods, demonstrating their ability to refine parameter estimation and improve model generalization. Future research should explore hybrid models that combine mechanistic kinetic equations with machine learning techniques (e.g., neural networks, or support vector regression), to better capture the biochemical processes underlying acidity changes in bananas.
DOI: https://doi.org/10.54167/tch.v19iEspecial.1847
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| format | Article |
| id | doaj-art-c7ef7f4812b94de8a7111b5cb158a8d0 |
| institution | Kabale University |
| issn | 1870-6606 2683-3360 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Universidad Autónoma de Chihuahua |
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| series | Tecnociencia Chihuahua |
| spelling | doaj-art-c7ef7f4812b94de8a7111b5cb158a8d02025-08-20T03:46:20ZengUniversidad Autónoma de ChihuahuaTecnociencia Chihuahua1870-66062683-33602025-06-0119Especial10.54167/tch.v19iEspecial.1847Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithmsAlejandro Kevin Méndez Castillo0https://orcid.org/0009-0001-8347-074XElizabeth Contreras López1https://orcid.org/0000-0002-9678-1264Jesús Guadalupe Pérez Flores2https://orcid.org/0000-0002-9654-3469Laura García Curiel3https://orcid.org/0000-0001-8961-2852Emmanuel Pérez Escalante4https://orcid.org/0000-0002-4268-9753Karla Soto Vega5https://orcid.org/0009-0003-1052-959XCarlos Ángel-Jijón6https://orcid.org/0000-0002-1047-9612Alicia Cervantes Elizarrarás7https://orcid.org/0000-0002-1432-2882Universidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de HidalgoUniversidad Autónoma del Estado de Hidalgo This research aimed to automate the fitting of kinetic models using genetic algorithms (GAs) to optimize the estimation of kinetic parameters—the reaction rate constant (k) and the initial value of total titratable acidity (TTA, C₀)—and enhance predictive accuracy. Experimental data were collected from bananas (Musa paradisiaca L.) at ripening stage 2 on Von Loesecke scale, measuring TTA at specific intervals, and three kinetic models—zero-order, first-order, and second-order—were fitted. For optimization, Python-based GA was used to minimize the mean squared error (MSE) and evaluate performance based on R2, AIC, and BIC. Results indicated that while the zero-order model provided the best statistical fit (R2 = 0.918, lowest AIC and BIC), validation on unseen data favored the second-order model, which exhibited superior predictive accuracy (test R2 = 0.9349, lowest test MSE). The integration of GAs enhanced the robustness of kinetic fitting by effectively navigating parameter space and avoiding local minima, outperforming traditional optimization methods, demonstrating their ability to refine parameter estimation and improve model generalization. Future research should explore hybrid models that combine mechanistic kinetic equations with machine learning techniques (e.g., neural networks, or support vector regression), to better capture the biochemical processes underlying acidity changes in bananas. DOI: https://doi.org/10.54167/tch.v19iEspecial.1847 https://revistascientificas.uach.mx/index.php/tecnociencia/article/view/1847ripening kineticsgenetic algorithmsparameter estimationpost-harvest modelingTTA decreasingfood quality |
| spellingShingle | Alejandro Kevin Méndez Castillo Elizabeth Contreras López Jesús Guadalupe Pérez Flores Laura García Curiel Emmanuel Pérez Escalante Karla Soto Vega Carlos Ángel-Jijón Alicia Cervantes Elizarrarás Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms Tecnociencia Chihuahua ripening kinetics genetic algorithms parameter estimation post-harvest modeling TTA decreasing food quality |
| title | Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| title_full | Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| title_fullStr | Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| title_full_unstemmed | Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| title_short | Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| title_sort | improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms |
| topic | ripening kinetics genetic algorithms parameter estimation post-harvest modeling TTA decreasing food quality |
| url | https://revistascientificas.uach.mx/index.php/tecnociencia/article/view/1847 |
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