Paviet-Salomon Y, Laguerre O, Duret S, Denis A, Mawilai P, Srisawat K, Derens-Bertheau E, Ndoye F T, Pongsuttiyakorn T, Rakmae S, Pun U K, Sirisomboon P, Pornchaloempong P, Chaomuang N
Université Paris-Saclay, INRAE, FRISE, 92761, Antony, France.
Department of Food Engineering, School of Engineering, King Mongkut's Institute of 7 Technology Ladkrabang, 10520, Bangkok, Thailand.
Data Brief. 2025 Jun 18;61:111803. doi: 10.1016/j.dib.2025.111803. eCollection 2025 Aug.
This study presents a detailed dataset collected during the international transport of mangoes (Mangifera indica L. cv. 'Nam Dok Mai Si-Thong') within a fully loaded Unit Load Device (ULD). The ULD contained 148 boxes (14 mangoes/box) and 31 boxes were instrumented by data loggers to monitor air and mango temperatures, as well as air humidity along the shipment. Measurements were recorded every 5 min throughout the 86.2 hour journey from Bangkok (Thailand) to Paris (France), it included cold storage, refrigerated transport, airport warehouse, air transport, and final delivery to the FRISE-INRAE laboratory in Paris suburban. At reception (Day 3), all boxes were stored at two temperatures (16 °C and 21 °C) over 15 days during which mango quality was assessed. Key quality attributes such as mass loss, peel color, pH, sugar content, and visual appearance were evaluated at Day 3, Day 6, Day 9, and Day 15 to highlight the impact of storage conditions on fruit quality. It is to be noticed that the same quality assessment was undertaken after harvest and before the cold storage in Thailand (Day 0). The dataset comprises raw and processed data files that include the temperature and humidity changes over time, as well as the quality evolution. These data are stored in series of txt files, with raw values and processed results i.e. min, max, average values, standard deviation and variance to facilitate future analysis. Additionally, the dataset includes series of jpeg files of mango images at different assessment days. This dataset is valuable for both practical and research purposes. For fruit exporters, it offers the insights into how different positions within a ULD can affect mango quality during a long-distance transport and implements certain measures to minimize quality degradation. For researchers, the dataset provides a base for numerical models validation, such as simplified thermal models and Computational Fluid Dynamics simulations. The international supply chain dataset is rare because of the complexity and the cost of implementation.
本研究展示了在一个满载的集装设备（ULD）内国际运输芒果（芒果品种为‘南多迈四号’，即 Mangifera indica L. cv. 'Nam Dok Mai Si-Thong'）期间收集的详细数据集。该ULD包含148箱芒果（每箱14个芒果），其中31箱安装了数据记录器，用于监测运输过程中的空气温度、芒果温度以及空气湿度。在从泰国曼谷到法国巴黎长达86.2小时的旅程中，每隔5分钟记录一次测量数据，旅程包括冷藏、冷藏运输、机场仓库、航空运输以及最终送达巴黎郊区的法国农业科学研究院（FRISE - INRAE）实验室。在接收货物时（第3天），所有箱子在两种温度（16°C和21°C）下储存15天，在此期间对芒果品质进行评估。在第3天、第6天、第9天和第15天评估了关键品质属性，如质量损失、果皮颜色、pH值、糖分含量和外观，以突出储存条件对果实品质的影响。需要注意的是，在泰国收获后和冷藏前（第0天）也进行了同样的品质评估。该数据集包括原始数据文件和处理后的数据文件，其中包含温度和湿度随时间的变化以及品质演变情况。这些数据存储在一系列txt文件中，包含原始值和处理结果，即最小值、最大值、平均值、标准差和方差，以便于未来分析。此外，该数据集还包括不同评估日期的芒果图像系列jpeg文件。这个数据集对于实际应用和研究目的都很有价值。对于水果出口商来说，它提供了关于ULD内不同位置如何在长途运输过程中影响芒果品质的见解，并有助于实施某些措施以尽量减少品质下降。对于研究人员而言，该数据集为数值模型验证提供了基础，如简化的热模型和计算流体动力学模拟。由于实施的复杂性和成本，国际供应链数据集较为罕见。
