Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
Alvarado Mamani, Ulises (Universidad Nacional del Altiplano de Puno. Escuela Profesional de Ingeniería Agroindustrial)
Tacuri, Jhon (Universidad Nacional del Altiplano de Puno. Escuela Profesional de Ingeniería Agroindustrial)
Coloma, Alejandro (Universidad Nacional del Altiplano de Puno. Escuela Profesional de Ingeniería Agroindustrial)
Gallegos Rojas, Edgar (Universidad Nacional del Altiplano de Puno. Escuela Profesional de Ingeniería Agroindustrial)
Callo, Herbert (Universidad Nacional del Altiplano de Puno. Escuela Profesional de Ingeniería Agroindustrial)
Valencia-Sullca, Cristina (Université de Bordeaux)
Curasi Rafael, Nancy (Universidad Peruana Unión)
Castillo Zambudio, Manuel (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)

Data:	2025
Resum:	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 (R = 0. 969; RMSE = 0. 007), while the optimal acidity model had four hidden layers with 32 neurons (R = 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.
Nota:	Altres ajuts: Programa Nacional de Investigación Científica y Estudios Avanzados (CONCYTEC) PE501082973-2023
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Fermentation ; PH ; Acidity ; CIELAB colour space ; Artificial neural networks
Publicat a:	Dairy, Vol. 6 Núm. 4 (august 2025) , p. 41, ISSN 2624-862X

DOI: 10.3390/dairy6040041

15 p, 748.6 KB

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