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Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
Author(s)
Alvarado, Ulises
Universidad Nacional del Altiplano
Tacuri, Jhon
Universidad Nacional del Altiplano
Coloma, Alejandro
Universidad Nacional del Altiplano
Gallegos Rojas, Edgar
Universidad Nacional del Altiplano
Callo, Herbert
Universidad Nacional del Altiplano
Valencia-Sullca, Cristina
Centre National de la Recherche Scientifique
Castillo, Manuel Z.
Universitat Autònoma de Barcelona
Date Issued
1 de agosto de 2025
Type
Article
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Volume
6
Issue
4
Series/Report No.
Dairy
Contained in
Dairy
ISSN
2624-862X
Abstract
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.
Financiamiento(s)
PE501082973-2023
Sponsor(s)
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.