Seabuckthorn berries are difficult to dry because the outermost surface is covered with a dense wax layer, which prevents moisture transfer during the drying process. In this study, uses of ultrasonic-assisted alkali (UA), pricking holes in the skin (PH) and their combination (UA + PH) as pretreatment methods prior to hot air drying and their effects on drying characteristics and quality attributes of seabuckthorn berries were investigated. Selected properties include color, microstructure, rehydration capacity, as well as total flavonoids, phenolics and ascorbic acid contents. Finally, the coefficient of variation method was used for comprehensive evaluation. The results showed that all pretreatment methods increased the drying rate; the combination of ultrasonic-assisted alkali (time, 15 min) and pricking holes (number, 6) (UA15 + PH6) had the highest drying rate that compared with the control group, the drying time was shortened by 33.05%; scanning electron microscopy images revealed that the pretreatment of UA could dissolve the wax layer of seabuckthorn berries, helped to form micropores, which promoted the process of water migration. All the pretreatments reduced the color difference and increased the lightness. The PH3 samples had the highest value of vitamin C content (54.71 mg/100 g), the UA5 and PH1 samples had the highest value of total flavonoid content (11.41 mg/g) and total phenolic content (14.20 mg/g), respectively. Compared to other pretreatment groups, UA15 + PH6 achieved the highest quality comprehensive score (1.013). Results indicate that UA15 + PH6 treatment is the most appropriate pretreatment method for improving the drying characteristics and quality attributes of seabuckthorn berries.

To realize the classification of sea buckthorn fruits with different water content ranges, a convolution neural network (CNN) detection model of sea buckthorn fruit water content ranges was constructed. In total, 900 images of seabuckthorn fruits with different water contents were collected from 720 seabuckthorn fruits. Eight classic network models based on deep learning were used as feature extraction for transfer learning. A total of 180 images were randomly selected from the images of various water content ranges for testing. Finally, the identification accuracy of the network model for the water content range of seabuckthorn fruit was 98.69%, and the accuracy on the test set was 99.4%. The program in this study can quickly identify the moisture content range of seabuckthorn fruit by collecting images of the appearance and morphology changes during the drying process of seabuckthorn fruit. The model has a good detection effect for seabuckthorn fruits with different moisture content ranges with slight changes in characteristics. The migration deep learning can also be used to detect the moisture content range of other agricultural products, providing technical support for the rapid nondestructive testing of moisture contents of agricultural products.