Modeling and Optimization of Microwave Vacuum Drying for Pinelliae Rhizoma: Integrating Drying Kinetics, Artificial Neural Networks, and Quality Preservation

This study investigated the effects of drying temperature and vacuum degree on the drying characteristics, color, rehydration ratio, nucleoside content, and microstructure of Pinelliae Rhizoma (PR) during microwave-vacuum drying (MVD). The results demonstrated that MVD significantly reduced the drying time and enhanced processing efficiency. Four different mathematical models and artificial neural network (ANN) predictive models are developed for further comparison. Among the models, the Midilli model provided the best fit for all samples (<i>R</i> ² = 0.99012, Pearson's <i>r</i> = 0.99509). However, the ANN approach has shown a better performance than the Midilli model. MVD resulted in a decrease in <i>L</i>* values, an increase in <i>a</i>* and <i>b</i>* values, and a higher BI index in dried Pinelliae Rhizoma (PR). No significant difference was observed in Δ<i>E</i>, indicating that the combined effects of temperature, vacuum degree, pigment stability, and water migration led to a similar degree of color change. Principal component analysis (PCA) and Pearson's correlation analysis revealed that MVD drying at 40°C and -60 kPa were the optimal conditions for efficiently drying Pinelliae Rhizoma (PR). Under this condition, most nucleosides are well preserved with the exception of adenosine. Overall, MVD technology, as demonstrated in this study, is a promising and suitable drying method in terms of processing efficiency for Pinelliae Rhizoma (PR).