Non-Destructive Quality Prediction of Fresh Goji Berries During Storage Using Dielectric Properties and ANN Modeling

We developed a model to predict the quality of fresh goji berries during storage by analyzing the correlations of their dielectric properties. The variations in these properties with storage temperature, time, and frequency were systematically characterized to inform the model. Leveraging these relationships, we developed a model to predict quality. The analysis integrated measurements of dielectric properties with assessments of texture and key physicochemical indicators. Results indicate that dielectric parameters exhibit significant frequency dependence. Complex impedance (Z), capacitance (Cp), and resistance (Rp) all decreased sharply with increasing frequency, with the most pronounced change observed in Cp. Conductance, G, and reactance, X, increased with frequency, reaching maximum increases of 360.86% and 87.79%, respectively. Under the specific test frequency of 163,280 Hz, a strong polynomial relationship was observed between the dielectric parameters and storage time, with all fitted models yielding Radj2 values above 0.94. The quality factor Q (a dimensionless number for the energy efficiency of a resonant circuit or medium) showed a near-perfect correlation with brittleness, while reactance, X, was correlated with springiness and cohesiveness, with correlation coefficients approaching 0.999 under the optimal test frequency. The constructed ANN model demonstrated high prediction accuracy for hardness, brittleness, elasticity, cohesiveness, chewiness, and soluble solids content (R2 > 0.97, MSE < 5%) but performed poorly in predicting adhesiveness, stickiness, and rebound elasticity (R2 < 0.9). The constructed LSSVM model showed good prediction performance for some indicators (hardness, springiness, cohesiveness, and SSC) (R2 > 0.94), but its prediction accuracy was low for brittleness and chewiness (R2 < 0.9). Overall, its performance and generalization ability were inferior to the ANN model. This study shows that ANN models based on dielectric properties establish a technical foundation for the non-destructive, automated monitoring of goji berry storage quality, thereby providing a critical tool for dynamic quality tracking and value assessment within integrated warehouse management systems.