Mou Xiaobin, Huang Xiaopeng, Ma Guojun, Luo Qi, Yang Xiaoping, Xin Shanglong, Wan Fangxin
College of Mechanical and Electronical Engineering, Gansu Agricultural University, Lanzhou 730070, China.
Foods. 2025 Aug 13;14(16):2807. doi: 10.3390/foods14162807.
Quality control of fresh during storage presents significant challenges, particularly regarding the unclear relationship between quality characteristics and storage conditions. This study analyzes the changes in qualitative and structural characteristics, including fruit hardness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc), under various storage conditions (temperature, duration, and initial maturity). We employed optimized Latin hypercubic sampling to develop radial basis function neural networks (RBFNNs) and Elman neural networks to establish predictive models for the quality characteristics of fresh wolfberry. Additionally, we applied the Particle Swarm Optimization (PSO) algorithm to determine the optimal solution for the constructed models. The results indicate a significant variation in how different storage conditions affect the quality characteristics. The established RBFNN predictive model exhibited the highest accuracy for TA and Vc during the storage of fresh wolfberry (R = 0.99, RMSE = 0.21 for TA; R = 0.99, RMSE = 0.19 for Vc), while the predictive performance for hardness and SSC was slightly lower (R = 0.98, RMSE = 385.78 for hardness; R = 0.94, RMSE = 2.611 for SSC). Multi-objective optimization led to the conclusion that the optimal storage conditions involve harvesting fruits at an initial maturity of 60% or greater and storing them for approximately 10 days at a temperature of 10 °C. Under these conditions, the fruit hardness was observed to be 15 N, with SSC at 17.5%, TA at 1.22%, and Vc at 18.5 mg/100 g. The validity of the prediction model was confirmed through multi-batch experimental verification. This study provides theoretical insights for predicting nutritional quality and informing storage condition decisions for other fresh fruits, including wolfberries.
新鲜枸杞在储存期间的质量控制面临重大挑战，尤其是质量特性与储存条件之间的关系尚不明确。本研究分析了在不同储存条件（温度、时长和初始成熟度）下，枸杞的定性和结构特性变化，包括果实硬度、可溶性固形物含量（SSC）、可滴定酸度（TA）和维生素C（Vc）。我们采用优化的拉丁超立方抽样法来开发径向基函数神经网络（RBFNNs）和埃尔曼神经网络，以建立新鲜枸杞质量特性的预测模型。此外，我们应用粒子群优化（PSO）算法来确定所构建模型的最优解。结果表明，不同储存条件对质量特性的影响存在显著差异。所建立的RBFNN预测模型在新鲜枸杞储存期间对TA和Vc的预测准确率最高（TA：R = 0.99，RMSE = 0.21；Vc：R = 0.99，RMSE = 0.19），而对硬度和SSC的预测性能略低（硬度：R = 0.98，RMSE = 385.78；SSC：R = 0.94，RMSE = 2.611）。多目标优化得出的结论是，最优储存条件为初始成熟度60%及以上时采收果实，并在10℃下储存约10天。在此条件下，观察到果实硬度为15 N，SSC为17.5%，TA为1.22%，Vc为18.5 mg/100 g。通过多批次实验验证证实了预测模型的有效性。本研究为预测其他新鲜水果（包括枸杞）的营养品质及指导储存条件决策提供了理论依据。
