Ginkgo biloba seeds were dried in microwave drier under different microwave powers (200, 280, 460, and 640 W) to determinate the drying kinetics and color changes during drying process. Drying curves of all samples showed a long constant rate period and falling rate period along with a short heating period. The effective moisture diffusivities were found to be 3.318 × 10⁻⁹ to 1.073 × 10⁻⁸m²/s within the range of microwave output levels and activation energy was 4.111 W/g. The L⁎ and b⁎ values of seeds decreased with drying time. However, a⁎ value decreased firstly and then increased with the increase of drying time. Artificial neural network (ANN) modeling was employed to predict the moisture ratio and color parameters (L⁎, a⁎, and b⁎). The ANN model was trained for finite iteration calculation with Levenberg-Marquardt algorithm as the training function and tansig-purelin as the network transfer function. Results showed that the ANN methodology could precisely predict experimental data with high correlation coefficient (0.9056–0.9834) and low mean square error (0.0014–2.2044). In addition, the established ANN models can be used for online prediction of moisture content and color changes of ginkgo biloba seeds during microwave drying process.

In current work, the influences of high-humidity hot air impingement blanching (HHAIB) time (60, 90, 120, and 150 s), drying temperature (60, 65, 70, and 75 °C), and air velocity (6, 9, and 12 m/s) on drying characteristics and quality attributes of broccoli florets were explored. Extreme learning machine (ELM) was employed to describe the drying behavior of broccoli florets. Results showed that proper HHAIB pretreatment can extensively increase drying rate compared to the control group (unblanched samples). The entire drying process of broccoli florets occurred in the falling period. Besides, proper HHAIB pretreatment can enhance vitamin C preservation, the color quality, and the rehydration capacity of dried broccoli florets. Based on error analysis results, the prediction accuracy of the optimal ELM model with 4-50-1 topology is found to be satisfied for the moisture ratio prediction of broccoli florets during air impingement drying process, with the R², ${\chi }^2$, and MSE reached to 0.9993, 8.04e⁻⁵, and 2.01e⁻⁴, respectively.