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.

Ultrasound is considered a physical technology that can be applied at various stages of food processing to optimise resources and reduce production time. In this study, the influence of ultrasonic treatments at different power levels (0 W, 100 W, 200 W, and 300 W) on three Isaño genotypes (yellow, yellow with purple eyes, and purple) during alcoholic fermentation was investigated. The main parameters assessed were yeast growth kinetics, ethanol production, and substrate consumption in an ultrasonic bath operating at a frequency of 50 kHz. The findings demonstrated enhanced yeast growth, accelerated substrate consumption, and increased ethanol production compared to the control (untreated sample). However, the impact of ultrasound on fermentation decreased as the power level increased. Notably, an ultrasonic power of 100 W applied over 84 h of fermentation resulted in the highest ethanol yield (10.36% v/v) in the purple Isaño genotype. In conclusion, ultrasonic treatment is a promising approach to improve the fermentation process of Isaño, potentially enabling its development as a functional beverage with both nutritional and therapeutic properties.