Physics driven digital twins to quantify the impact of pre and postharvest variability on the end quality evolution of orange fruit

Abstract

Currently, there are differences in the quality loss between individual fruit upon arrival at retail. These differences in fruit quality stem from pre-harvest biological variability between individual fruit at harvest and postharvest variations in hygrothermal conditions between refrigerated shipments. The impact of these pre-harvest biological and postharvest variability on the final quality of each fruit that reaches the consumers remains largely uncharted. Here, we address this gap by developing physics-based digital twins of orange fruit to unveil how pre-harvest and postharvest variability affect the final fruit quality upon arrival at retail. We use the Markov chain Monte Carlo method to generate a realistic 'virtual' population of 1000 individual orange fruits at harvest. We then quantify the impact of pre-harvest biological variability and variations in hygrothermal conditions between shipments on several orange quality metrics, including mass loss, fruit quality index (FQI), remaining shelf life (RSL), chilling injury severity (CI), total soluble solids (TSS), color, and Mediterranean fruit fly (MFF) mortality. We show that pre-harvest biological variability causes variations in mass loss of oranges at retail by up to 1.2%, FQI by up to 5% and RSL by more than 2 days. Our results demonstrate that postharvest variability between shipments causes high variations in mass loss of oranges at retail by up to 4%, FQI by more than 20%, RSL up to 3 days, and CI up to 5%. We also show that compared to pre-harvest biological variability, postharvest variability between shipments could increase the variations in RSL of oranges at retail by 75%, FQI by 50%, and mass loss by ~10%. This work helps improve our understanding of the variability in the end fruit quality upon arrival at retail.