S23 - Session O4 - Mango shelf-life modelling

Authors: Yiru Chen *, Daryl Joyce, Neil White, Philippa Bryant, Aljay Valida, Hung Duong, Andrew Macnish, Lawrence Smith

Short shelf-life of fresh produce can disappoint wholesalers, retailers, and consumers, damage reputations, and lessen repeat purchases. With contemporary technologies, monitoring supply chain conditions from farm to retail is totally accessible to supply chain stakeholders. Thereby, agile decision-making in a 'first expired first out' context is enabled by real time data collection. Remaining shelf-life (RSL) models were developed for mango fruit using data collected in laboratory trials simulating real-world export air and sea freight conditions. Storage duration and temperature matrices covering generally recognised biologically safe ranges were applied for two mango varieties. Harvest time and a phytosanitary treatment were also considered. Modelling was validated by repeat experiments and with data sub-sets and verified with real world shipment monitoring data. Simulation experiments showed that specific regression models were required to account for cultivar and harvest time differences. Prediction intervals (PI) at the 90% confidence level were +- 3.2 days for 'R2E2' and +- 2.8 days for 'Kensington Pride', respectively. Root mean square errors (RMSE) at 90% were +- 4.1 days for. 'R2E2' and +- 4.8 days for 'Kensington Pride'. Dry matter content at harvest as a co-variable did not consistently improve RSL predictions. A web-based dashboard and a mobile phone application were developed to demonstrate the utility of the RSL modelling to industry.