Predicting salt uptake in dry-cured ham using longitudinal modeling and X-ray technology

This study proposes a novel approach to optimize the salting duration of dry-cured hams by integrating longitudinal data and non-invasive X-ray technology. A total of 392 green hams were monitored to quantify fat content, weight, and salt content at 5, 7, 10, 12, and 14 days of salting. A linear mixed model (LMM) was developed to predict salt uptake based on time, initial fat content, and weight loss at day 5. The model accurately predicted individualized salting durations to reach a target salt content of 3.20%, achieving an average salt content of 3.16%, standard deviation of 0.26%, and a marginal model predictive performance of R_m²=0.911. By contrast, the traditional method resulted in an average salt content of 3.77% with a standard deviation of 0.61%. Compared to the traditional salting rule of 1 day per kilogram, the LMM-based approach significantly reduced variability in final salt content while maintaining high accuracy. This predictive method supports the adoption of Industry 4.0 practices in ham production, enabling data-driven decisions to improve process standardization and product quality.