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Article

Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models

by
Abiola Owoyemi
1,2,
Ron Porat
1,*,
Amnon Lichter
1,
Adi Doron-Faigenboim
3,
Omri Jovani
4,5,
Noam Koenigstein
4 and
Yael Salzer
5
1
Department of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
2
Robert H. Smith Faculty of Agricultural, Food and Environmental Sciences, The Hebrew University of Jerusalem, Rehovot 76100, Israel
3
Genomics and Bioinformatics Unit, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
4
Department of Industrial Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
5
Department of Growing, Production and Environmental Engineering, ARO, The Volcani Institute, Rishon LeZion 7528809, Israel
*
Author to whom correspondence should be addressed.
Horticulturae 2022, 8(7), 570; https://doi.org/10.3390/horticulturae8070570
Submission received: 31 May 2022 / Revised: 14 June 2022 / Accepted: 20 June 2022 / Published: 22 June 2022
(This article belongs to the Special Issue Postharvest Management of Citrus Fruit)
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Abstract

We conducted a large-scale, high-throughput phenotyping analysis of the effects of various preharvest and postharvest features on the quality of ‘Valencia’ oranges in order to develop shelf-life prediction models. Altogether, we evaluated 10,800 oranges (~3.6 tons) harvested from three orchards at different periods and conducted 151,200 measurements of 14 quality parameters. The storage time was the most important feature affecting fruit quality, followed by the yield, storage temperature, humidity, and harvest time. The storage time and temperature features significantly affected (p < 0.001) all or most of the tested quality parameters, whereas the harvest time, yield, and humidity conditions significantly affected several particular quality parameters, and the selection of rootstocks had no significant effect at all. Five regression models were evaluated for their ability to predict fruit quality based on preharvest and postharvest features. Non-linear Support Vector Regression (SVR) combined with a data-balancing approach was found to be the most effective approach. It allowed the prediction of fruit-acceptance scores among the full data set, with a root mean square error (RMSE) of 0.195 and an R2 of 0.884. The obtained data and models should assist in determining the potential storage times of different batches of fruit.
Keywords: citrus; intelligent logistics; modeling; orange; postharvest citrus; intelligent logistics; modeling; orange; postharvest

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MDPI and ACS Style

Owoyemi, A.; Porat, R.; Lichter, A.; Doron-Faigenboim, A.; Jovani, O.; Koenigstein, N.; Salzer, Y. Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models. Horticulturae 2022, 8, 570. https://doi.org/10.3390/horticulturae8070570

AMA Style

Owoyemi A, Porat R, Lichter A, Doron-Faigenboim A, Jovani O, Koenigstein N, Salzer Y. Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models. Horticulturae. 2022; 8(7):570. https://doi.org/10.3390/horticulturae8070570

Chicago/Turabian Style

Owoyemi, Abiola, Ron Porat, Amnon Lichter, Adi Doron-Faigenboim, Omri Jovani, Noam Koenigstein, and Yael Salzer. 2022. "Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models" Horticulturae 8, no. 7: 570. https://doi.org/10.3390/horticulturae8070570

APA Style

Owoyemi, A., Porat, R., Lichter, A., Doron-Faigenboim, A., Jovani, O., Koenigstein, N., & Salzer, Y. (2022). Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models. Horticulturae, 8(7), 570. https://doi.org/10.3390/horticulturae8070570

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