This study aimed to compare tilapia fish cake drying and sterilization conditions (105, 115, and 121 °C) on the quality of the cakes. The impacts of volatile flavor substances, the chroma value, quality and structure characteristics, microscopic structure, and the types and content of volatile flavor substances were also analyzed. The results showed that after drying and sterilization, the L* value, W value and delta-E value of fish cakes decreased significantly from 77.12 to 64.77, 66.21 to 52.57, 10.46 to 24.50, respectively. However, a* value and b* value increased significantly from 0.30 to 6.97 and 24.85 to 30.89, respectively. The elasticity, hardness, and chewiness increased significantly with the drying process but decreased significantly with the increased sterilization temperature. Scanning electron microscopy results showed that the internal pores of the fish cakes became smaller, and the tissue structure was closer after drying. Gas chromatography—ion mobile spectrometry analysis identified a total of 36 volatile flavor compounds. Among these, ketones comprised the largest content, aldehydes represented the largest variety, and all volatile compounds contributed significantly to the flavor of fish cake. PCA results and nearest-neighbor fingerprint analysis showed that there were obvious differences in volatile flavor compounds between different treatments. In summary, this study conducted a detailed comparative analysis of the quality and flavor of fish cakes subjected to different processing methods. These findings contribute suggestions for sterilization temperatures in industrial production processes.

In the experiment of this article, the artificial neural network (ANN) was used to establish the sturgeon bone marrow drying model. Further, the effects of different temperatures (40, 60, and 80°C), humidities (0, 20, and 40%), and air velocities (8, 16, and 25 m/s) on the drying characteristics of sturgeon bone marrow were stud-ied. The studies had shown that with the increase of drying temperature, the acceleration of air velocity, and the decrease of humidity, the sturgeon bone marrow can be dried in the shortest period of 100 min. This study used ANN to feasibly predict dried sturgeon bone marrow moisture ratio, based on the time, temperature, humidity, and air velocity drying inputs. The results revealed that 11 hidden neurons were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R value 0.996. This model correctly predicted the optimal drying conditions and established that temperature is the single most significant factor in determining the drying time of sturgeon bone marrow. It is expected that this system will have broader application in other food drying requirements.