Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labourintensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt...
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
Multidisciplinary Digital Publishing Institute (MDPI)
2025
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881510187220598784 |
|---|---|
| author | Alvarado, Ulises Tacuriti, Juan Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Rafael, Nancy Curasi Castillo, Manuel |
| author_browse | Alvarado, Ulises Callo, Herbert Castillo, Manuel Coloma, Alejandro Gallegos Rojas, Edgar Rafael, Nancy Curasi Tacuriti, Juan Valencia-Sullca, Cristina |
| author_facet | Alvarado, Ulises Tacuriti, Juan Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Rafael, Nancy Curasi Castillo, Manuel |
| author_sort | Alvarado, Ulises |
| collection | Repositorio institucional |
| description | Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labourintensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 ◦C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R2 = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R2 = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial
yoghurt production. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.upeu.edu.pe:20.500.12840/9883 |
| institution | UPeU |
| language | English |
| ocde_area_str_mv | Ciencias médicas y de la salud |
| ocde_disciplina_str_mv | Ciencias de la salud |
| ocde_str_mv | 3.03.00 |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| publisherStr | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation Alvarado, Ulises Tacuriti, Juan Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Rafael, Nancy Curasi Castillo, Manuel Fermentation PH Acidity CIELAB colour space Artificial neural networks Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labourintensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 ◦C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R2 = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R2 = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial yoghurt production. Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica Programa Nacional de Investigación Científica y Estudios Avanzados PE501082973-2023 2025-08-01 2026-03-10T20:08:05Z 2026-03-10T20:08:05Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://hdl.handle.net/20.500.12840/9883 https://doi.org/10.3390/dairy6040041 eng urn:issn:2624-862X info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Multidisciplinary Digital Publishing Institute (MDPI) PE oai:repositorio.upeu.edu.pe:20.500.12840/9883 2026-06-11T09:36:45Z |
| spellingShingle | Fermentation PH Acidity CIELAB colour space Artificial neural networks Alvarado, Ulises Tacuriti, Juan Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Rafael, Nancy Curasi Castillo, Manuel Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title_full | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title_fullStr | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title_full_unstemmed | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title_short | Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation |
| title_sort | development of a hybrid system based on the cielab colour space and artificial neural networks for monitoring ph and acidity during yogurt fermentation |
| topic | Fermentation PH Acidity CIELAB colour space Artificial neural networks |
| topic_facet | Fermentation PH Acidity CIELAB colour space Artificial neural networks |
| url | https://hdl.handle.net/20.500.12840/9883 https://doi.org/10.3390/dairy6040041 |
| work_keys_str_mv | AT alvaradoulises developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT tacuritijuan developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT colomaalejandro developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT gallegosrojasedgar developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT calloherbert developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT valenciasullcacristina developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT rafaelnancycurasi developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation AT castillomanuel developmentofahybridsystembasedonthecielabcolourspaceandartificialneuralnetworksformonitoringphandacidityduringyogurtfermentation |