Work

AHP-optimized processing technology and quality evaluation system for Rehmanniae Radix

Year: 2025

Type: article

Abstract: Traditional Chinese herbal processing techniques, such as the Nine-Steaming Nine-Sun-Drying method for Rehmanniae Radix, face challenges due to prolonged processing cycles, high energy consumption, and inconsistent product quality. This study innovatively developed an intelligent energy-saving multi-functional steaming-drying integrated system based on modular integration concepts. The system facilitates automated control of steaming, gradient drying, and UV sterilization processes via coordinated control of steam generation, vacuum moistening, temperature gradients, and real-time monitoring. Using Rehmanniae Radix as a model herb, we designed three new processes with optimized parameters: steaming duration, gradient drying temperatures, and cycle numbers. Data on polysaccharide content, moisture content, and chromaticity parameters were systematically analyzed using sulfuric acid-phenol method, moisture measurement, and measurement of chromaticity. The analysis revealed parameter threshold effects, demonstrated the superiority of gradient drying, and quantified process endpoints. AHP-based multidimensional evaluation identified a 4 h steaming at 110–120 °C followed by gradient drying (75 °C/2 h → 65 °C/3 h → 55 °C/3 h) as the optimal five-cycle procedure, which yielded 45.88% polysaccharides, L ∗ value of 18.75, b ∗ value of 4.02, 11.68% (Wet based) moisture content, and energy consumption of 1.50 kW⋅h/kg. The modular design with adjustable parameters enables adaptation to other herbs requiring steaming-drying. This establishes a universal technical platform for transforming herbal processing from empirical practice to data-driven standardization. • Intelligent steaming-drying system achieves reduces energy consumption by over 40% compared to traditional methods energy reduction. • Synergistic steaming and gradient drying enhance Rehmanniae Radix quality. • Optimization of processing indicators using the AHP model.

Source: LWT

DOI: 10.1016/j.lwt.2025.118325