Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models
Abiola Owoyemi,
Ron Porat,
Amnon Lichter,
Adi Doron-Faigenboim,
Omri Jovani,
Noam Koenigstein,
Yael Salzer
Affiliations
Abiola Owoyemi
Department of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
Ron Porat
Department of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
Amnon Lichter
Department of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
Adi Doron-Faigenboim
Genomics and Bioinformatics Unit, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
Omri Jovani
Department of Industrial Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 6997801, Israel
Noam Koenigstein
Department of Industrial Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 6997801, Israel
Yael Salzer
Department of Growing, Production and Environmental Engineering, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
We conducted a large-scale, high-throughput phenotyping analysis of the effects of various pre-harvest and postharvest features on the quality of ‘Rustenburg’ navel oranges, in order to develop shelf-life prediction models to enable the use of the First Expired, First Out logistics strategy. The examined pre-harvest features included harvest time and yield, and the examined postharvest features included storage temperature, relative humidity during storage and duration of storage. All together, we evaluated 12,000 oranges (~4 tons) from six different orchards and conducted 170,576 measurements of 14 quality parameters. Storage time was found to be the most important feature affecting fruit quality, followed by storage temperature, harvest time, yield and humidity. The examined features significantly affected (p 2 of 0.891.
