Alqahtani Nashi K, Alkhamis Bayan, Alnemr Tareq M, Mohammed Maged
Department of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia.
Date Palm Research Center of Excellence, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Heliyon. 2025 Jan 30;11(4):e42373. doi: 10.1016/j.heliyon.2025.e42373. eCollection 2025 Feb 28.
The postharvest preservation of fresh produce is crucial for enhancing food sustainability and security. The study investigates the combined effects of coating with gum Arabic (GA), storage temperature, and packaging methods on the quality of Barhi date during storage. In addition, the artificial neural network (ANN) model was used to predict fruit quality parameters, including fruit weight, volume, density, weight loss, hardness, decay percentage, moisture content, pH, Total soluble solid, water activity, color parameters, color difference, and browning index based on the coating and storage conditions and the initial fruit weight, size, moisture content, total soluble solids, and color parameters at the beginning of storage. The findings indicated that vacuum packaging, coating with 10 % GA concentration, and cold storage were the most effective combinations for prolonging shelf life and preserving the quality parameters of stored Barhi dates. The implemented ANN model effectively predicted most fruit quality parameters, closely corresponding with observed data across various storage environments, as indicated by the low values of the evaluation metrics, i.e., mean absolute error, mean absolute percentage error, relative error, and root mean squared error. The R values observed for the quality parameters of fruit weight (0.951), volume (0.746), density (0.735), weight loss (0.989), hardness (0.967), decay percentage (0.962), moisture content (0.901), pH (0.965), total soluble solids (0.973) water activity (0.859), and color parameters of L∗ (0.978), a∗ (0.784), b∗, ΔE∗ (0.955), and browning index (0.951), validate the precision and dependability of the ANN models in their ability to predict the quality attributes of Barhi date fruits. The study outcomes contribute to food quality and supply chain management by finding the best combination of edible GA coating and storage conditions. Inaddition, predicting fruit quality during storage helps maintain their quality and reduce postharvest losses.
新鲜农产品的采后保鲜对于提高粮食可持续性和安全性至关重要。本研究调查了用阿拉伯胶（GA）包衣、储存温度和包装方法对Barhi枣在储存期间品质的综合影响。此外，基于包衣和储存条件以及储存开始时的初始果实重量、大小、水分含量、总可溶性固形物和颜色参数，使用人工神经网络（ANN）模型预测果实品质参数，包括果实重量、体积、密度、重量损失、硬度、腐烂率、水分含量、pH值、总可溶性固形物、水分活度、颜色参数、色差和褐变指数。研究结果表明，真空包装、10%GA浓度包衣和冷藏是延长货架期和保持储存的Barhi枣品质参数的最有效组合。实施的ANN模型有效地预测了大多数果实品质参数，与各种储存环境下的观测数据密切对应，评估指标（即平均绝对误差、平均绝对百分比误差、相对误差和均方根误差）的值较低表明了这一点。果实重量（0.951）、体积（0.746）、密度（0.735）、重量损失（0.989）、硬度（0.967）、腐烂率（0.962）、水分含量（0.901）、pH值（0.965）、总可溶性固形物（0.973）、水分活度（0.859）以及颜色参数L∗（0.978）、a∗（0.784）、b∗、ΔE∗（0.955）和褐变指数（0.951）的R值，验证了ANN模型在预测Barhi枣果实品质属性方面的精度和可靠性。研究结果通过找到可食用GA包衣和储存条件的最佳组合，为食品质量和供应链管理做出了贡献。此外，预测储存期间的果实品质有助于保持其品质并减少采后损失。
