Modeling carbon dioxide solubility in fruit and vegetable juices for supercritical carbon dioxide pasteurization

Supercritical carbon dioxide (SC-CO₂) is a promising non-thermal technology for pasteurizing fruit juice and liquid products, assuring microbial stabilization with minimal impact on quality. A key factor influencing its efficacy is the solubility of CO₂ in the juice matrix. In this study, a predictive model for CO₂ solubility was developed using a factorial experimental design with additional points, evaluating five variables: pressure (10–14 MPa), temperature (35–45 °C), glucose (0–15 g/100 mL), sodium chloride (0–3 g/100 mL), and citric acid (0–6 g/100 mL). A total of 21 experimental conditions were tested with model solutions replicating typical fruit juices composition. CO₂ solubility was measured using a high-pressure system with validated sampling protocol. Values ranged from 3.832 to 5.730 gCO₂/100 g. The resulting linear regression model (R² = 0.897; RMSE = 0.148 gCO₂/100 g), identified glucose and sodium chloride as significant negative factors affecting solubility, while citric acid was excluded from the model due to its lack of significant impact. The model was validated on eight real fruit and vegetable juices, with CO₂ solubility predictions based on °Brix and ash content as proxies for glucose and salt. Validation showed good agreement with measured values, yielding a mean absolute error of 0.141 g CO₂/100 g and an R² of 0.85 calculated on the mean of three replicates per sample. These results confirm the model's accuracy and practical applicability, supporting its use in process design and optimization for SC-CO₂ pasteurization. This study contributes to a better understanding of CO₂ behavior in real juice matrices and provides a practical tool for tailoring SC-CO₂ processes based on juice composition.