Developing a novel artificial model to predict the foaming properties and β-carotene content of lucuma (Pouteria lucuma) during foam-mat drying and process optimization

Abstract

Drying fruit puree by the foam drying method has become popular due to its simplicity, low cost, short drying time, and low thermal degradation. The objective of the study was to investigate the effect of foaming conditions on foam properties (foam expansion, foam density) and content of β-carotene in lucuma powder using Box-Behnken design (BBD) with 3 factors and 3 levels, including water:lucuma ratio (1:1–3:1), egg albumin concentration (EA, 5–15 %), and xanthan gum (XG, 0.1–0.3 %). Response surface methodology (RSM) and artificial neural network (ANN) were used for model establishment. The results showed that as the EA increased, the foam volume increased significantly, while the foam density decreased. The ANN-coupled BBD model structure of 3–10-3 demonstrated a high level of accuracy in predicting the impact of foaming formulation on responses, with a coefficient of determination exceeding 0.99. The optimal conditions by stimulation multiple-objective RSM for lucuma foam-mat drying were achieved with a water:lucuma ratio, EA, and XG of 2.53:1, 10.8 %, and 0.22 %, respectively. Based on these ideal conditions, the foam density, foam expansion, and β-carotene content of the dried powder were found to be 0.23 ± 0.04 g/mL, 228 ± 2 %, and 237.1 ± 0.1 μg/g, respectively. The obtained experimental values were very close to the model-predicted results, with very low differences identified when the validation was performed. These findings provide information for controlling the drying process using an artificial model and further applying lucuma powder in various fields in the food industry.

Keywords

• Lucuma
• Response surface methodology
• Artificial neural network
• β-carotene
• Foam-mat drying