Drying is one of the most common methods for processing and preserving sea cucumber. Based on the research of pretreatment and drying process, this paper develops a dried sea cucumber product that can be quickly rehydrated in only 8 hours. By setting the pretreatment at heated water temperature of 80°C and the drying temperature from 30°C to 60°C, the fitting model is found by comparing empirical formulas and artificial neural networks. The ANN-based model was demonstrated to fit the experimental data for the adequate drying of sea cucumber. Following the increased drying temperature, the drying time was decreased and the rehydration ratio was increased. The sensory evaluation and texture properties dried at 40°C and 50°C were much better than those dried at 30°C and 60°C. Microstructure of rehydrated dried sample showed that increasing the temperature leads to the increase of fiber pore space, and the rehydration rate increases. The results of drying time and the rehydration properties of sea cucumber showed that 50°C is the best drying condition for hot-air drying of sea cucumber. The developed rapid rehydration dried sea cucumber can effectively simplify the rehydration time and steps of the dried sea cucumber and improve the quality of the sea cucumber, and it is considered to be a good technology for drying the sea cucumber and making a fast rehydration dried sea cucumber that can be further used for value-added product.
The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method.
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