Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
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.
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BibTeX
@article{alvarado2026development,
title = {{Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation}},
author = {Ulises Alvarado and Jhon Tacuri and Alejandro Coloma and Edgar Gallegos Rojas and Herbert Callo and Cristina Valencia-Sullca and Nancy Curasi Rafael and Manuel Castillo},
journal = {Dairy},
year = {2026},
month = oct,
doi = {10.3390/dairy6040041},
url = {https://hal.science/hal-05842746v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
AU - Ulises Alvarado
AU - Jhon Tacuri
AU - Alejandro Coloma
AU - Edgar Gallegos Rojas
AU - Herbert Callo
AU - Cristina Valencia-Sullca
AU - Nancy Curasi Rafael
AU - Manuel Castillo
PY - 2026
DA - 2026/10/28
JO - Dairy
DO - 10.3390/dairy6040041
UR - https://hal.science/hal-05842746v1
AB - 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.
SN - 2624-5647
ER -