A decision support tool for shelf-life determination of strawberries using hyperspectral imaging technology

Anastasia Ktenioudaki, Carlos A. Esquerre, Cecilia M. Do Nascimento Nunes, Colm P. O'Donnell

Research output: Contribution to journalArticlepeer-review


Due to high perishability and poor distribution management, strawberry is one of the most frequently discarded fruits. The aim of this study was to develop a non-destructive system for accurate estimation of shelf-life of strawberries using hyperspectral imaging technology. Harvested strawberries were stored for 9 day at five different temperatures. Shelf-life was calculated based on subjective visual evaluation of appearance attributes (colour, shrivelling, and decay) using a rating scale. Hyperspectral images of strawberries were obtained during cold storage, using a novel handheld push broom line-scanning hyperspectral camera. The model developed by partial least square regression (PLSR) with selected spectra was used to predict appearance scores of strawberries with a coefficient of determination of prediction (R2p) of 0.97 and root mean square error of prediction (RMSEp) of 0.17. The appearance scores from the PLSR model were used to develop a model based on first-order kinetics and Arrhenius equations to predict the remaining shelf-life of the strawberries. The models predicted remaining shelf-life with R2p of 0.86 and RMSEp of 1.4 days. Prediction models were also developed for other quality attributes and biochemical properties (weight loss, ascorbic acid, and soluble solids). The results from this study support the development a non-destructive system for the accurate estimation of shelf-life of strawberries. A system that could potentially offer objective quality assessment as product moves through the supply chain, aiding decision making and facilitating proactive actions to be taken with the aim of minimising loss/waste; benefiting food supply chain stakeholders across the globe and supporting the drive for zero waste.

Original languageEnglish
Pages (from-to)105-117
Number of pages13
JournalBiosystems Engineering
Publication statusPublished - Sep 2022
Externally publishedYes


  • Food quality
  • Food supply chain
  • Food waste
  • Prediction modelling
  • Process analytical technologies


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