Zhang Jikai, Zheng Xia, Xiao Hongwei, Shan Chunhui, Li Yican, Yang Taoqing
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China.
Foods. 2024 Jan 29;13(3):434. doi: 10.3390/foods13030434.
In this paper, the effects on drying time (Y), the color difference (Y), unit energy consumption (Y), polysaccharide content (Y), rehydration ratio (Y), and allantoin content (Y) of yam slices were investigated under different drying temperatures (50-70 °C), slice thicknesses (2-10 mm), and radiation distances (80-160 mm). The optimal drying conditions were determined by applying the BP neural network wolf algorithm (GWO) model based on response surface methodology (RMS). All the above indices were significantly affected by drying conditions ( < 0.05). The drying rate and effective water diffusion coefficient of yam slices accelerated with increasing temperature and decreasing slice thickness and radiation distance. The selection of lower temperature and slice thickness helped reduce the energy consumption and color difference. The polysaccharide content increased and then decreased with drying temperature, slice thickness, and radiation distance, and it was highest at 60 °C, 6 mm, and 120 mm. At 60 °C, lower slice thickness and radiation distance favored the retention of allantoin content. Under the given constraints (minimization of drying time, unit energy consumption, color difference, and maximization of rehydration ratio, polysaccharide content, and allantoin content), BP-GWO was found to have higher coefficients of determination ( = 0.9919 to 0.9983) and lower (reduced by 61.34% to 80.03%) than RMS. Multi-objective optimization of BP-GWO was carried out to obtain the optimal drying conditions, as follows: temperature 63.57 °C, slice thickness 4.27 mm, radiation distance 91.39 mm, corresponding to the optimal indices, as follows: Y = 133.71 min, Y = 7.26, Y = 8.54 kJ·h·kg, Y = 20.73 mg/g, Y = 2.84 kg/kg, and Y = 3.69 μg/g. In the experimental verification of the prediction results, the relative error between the actual and predicted values was less than 5%, proving the model's reliability for other materials in the drying technology process research to provide a reference.
本文研究了不同干燥温度（50 - 70°C）、切片厚度（2 - 10 mm）和辐射距离（80 - 160 mm）对山药片干燥时间（Y）、色差（Y）、单位能耗（Y）、多糖含量（Y）、复水率（Y）和尿囊素含量（Y）的影响。基于响应面法（RMS）应用BP神经网络灰狼算法（GWO）模型确定了最佳干燥条件。上述所有指标均受干燥条件显著影响（<0.05）。山药片的干燥速率和有效水分扩散系数随温度升高、切片厚度减小和辐射距离减小而加快。选择较低温度和切片厚度有助于降低能耗和色差。多糖含量随干燥温度、切片厚度和辐射距离先增加后降低，在60°C、6 mm和120 mm时最高。在60°C时，较低的切片厚度和辐射距离有利于尿囊素含量的保留。在给定约束条件下（最小化干燥时间、单位能耗、色差，最大化复水率、多糖含量和尿囊素含量），发现BP - GWO的决定系数较高（=0.9919至0.9983），且比RMS的较低（降低了61.34%至80.03%）。对BP - GWO进行多目标优化以获得最佳干燥条件，如下：温度63.57°C，切片厚度4.27 mm，辐射距离91.39 mm，对应最佳指标如下：Y = 133.71分钟，Y = 7.26，Y = 8.54 kJ·h·kg，Y = 20.73 mg/g，Y = 2.84 kg/kg，Y = 3.69 μg/g。在预测结果的实验验证中，实际值与预测值之间的相对误差小于5%，证明该模型可为干燥技术过程研究中的其他材料提供参考。
