This study presents a data-driven framework to predict and optimize the quality of date juice (DJ) produced from two commercially important Saudi cultivars (Sukkary and Khlass) using physicochemical and processing variables as model inputs. A total of 1600 experimental runs were performed by systematically varying initial fruit moisture content, extraction temperature (20, 40, 60, and 80 °C), mixing velocity (10, 20, 30, 40, and 50% of maximum speed), and date-to-water ratios (1:1, 1.5, 2, 2.5, and 3 w/w). The produced juices were characterized at 25 °C for water activity, moisture content, density, pH, total soluble solids (°Brix), turbidity, viscosity, hydroxymethylfurfural (HMF), browning index, extraction time, electrical energy consumption, and an integrated Quality Index (Qi). A feed-forward artificial neural network (ANN; 7–15–1) with a hyperbolic tangent transfer function was developed and validated using normalized datasets, and its performance was benchmarked against multiple linear regression (MLR). The ANN consistently outperformed MLR for Qi prediction, achieving higher coefficients of determination and lower error indices across training, testing, and validation, indicating strong generalization and minimal overfitting. Sensitivity analysis highlighted total soluble solids, moisture content, and HMF as the most influential predictors of Qi. Optimal juice quality (Qi ≥ 0.91) was repeatedly achieved under moderate thermal conditions (≈60 °C), with 40% mixing velocity and a 1:2.5 date-to-water ratio, providing a practical operating window for producing juice at the target °Brix while limiting thermal quality deterioration. Overall, the proposed ANN-based model provides an actionable decision-support tool for process optimization and quality standardization, supporting the transition of date-juice manufacturing toward Industry 4.0 through data-driven monitoring and adaptive control strategies.
Elamshity et al. (2026) studied this question.