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 labour-intensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt...
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| Formato: | Artículo |
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Multidisciplinary Digital Publishing Institute (MDPI)
2025
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| author | Alvarado, Ulises Tacuri, Jhon Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Curasi Rafael, Nancy Castillo, Manuel Z. |
| author_browse | Alvarado, Ulises Callo, Herbert Castillo, Manuel Z. Coloma, Alejandro Curasi Rafael, Nancy Gallegos Rojas, Edgar Tacuri, Jhon Valencia-Sullca, Cristina |
| author_facet | Alvarado, Ulises Tacuri, Jhon Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Curasi Rafael, Nancy Castillo, Manuel Z. |
| author_sort | Alvarado, Ulises |
| collection | Producción científica |
| description | Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. 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 | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/2371 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| publisherStr | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/23712026-10-30T17:09:11ZDevelopment of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt FermentationAlvarado, UlisesTacuri, JhonColoma, AlejandroGallegos Rojas, EdgarCallo, HerbertValencia-Sullca, CristinaCurasi Rafael, NancyCastillo, Manuel Z.fermentationpHacidityCIELAB colour spaceartificial neural networksMonitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. 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.This research was supported by the Consejo Nacional de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n Tecnol\u00F3gica (CONCYTEC) and the Programa Nacional de Investigaci\u00F3n Cient\u00EDfica y Estudios Avanzados (PROCIENCIA), under Contrato N\u00B0 PE501082973-2023.Article10.3390/dairy60400412-s2.0-105014521127https://hdl.handle.net/20.500.15037/2371enDairyDairy2624-862XPE501082973-2023PE501082973-2023Multidisciplinary Digital Publishing Institute (MDPI)2025-08-012026-06-27T00:58:54Z2026-06-27T00:58:54Z |
| spellingShingle | fermentation pH acidity CIELAB colour space artificial neural networks Alvarado, Ulises Tacuri, Jhon Coloma, Alejandro Gallegos Rojas, Edgar Callo, Herbert Valencia-Sullca, Cristina Curasi Rafael, Nancy Castillo, Manuel Z. 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/20.500.15037/2371 |
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