Dataset of air cargo supply chain conditions and fruit quality evolution: Case study of mango shipment from Thailand to France

Abstract

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.