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Digital twins enable the quantification of the trade-offs in maintaining citrus quality and marketability in the refrigerated supply chain.

Chandrima ShrivastavaTarl BerryPaul CronjeSeraina SchudelThijs Defraeye
Published in: Nature food (2022)
Supply chains of fresh fruit must maintain a very narrow window of hygrothermal conditions after harvest. Any excursions outside this range can markedly lower the consumer acceptability of the fruit. However, the loss in fruit quality and marketability largely remains invisible to stakeholders throughout the supply chain. Here we developed a physics-based digital twin of citrus fruit to pinpoint when, why and to what extent fruit quality and marketability are reduced. Sensor data on 47 commercial shipments are thereby translated into actionable metrics for supply chain stakeholders by mapping the variability using principal component analysis. We unveiled a large spread (between 3% and 60%) in the shipments for different metrics of quality and marketability. Half of the shipments currently lie outside the ideal trade-off range between maintaining quality, killing fruit fly larvae and avoiding chilling injury. The digital twin technology opens the possibility to obtain the real-time coupling with sensor data to monitor food quality and marketability.
Keyphrases
  • big data
  • machine learning
  • artificial intelligence
  • data analysis
  • climate change
  • ionic liquid