Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models
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 exa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/13/1840 |
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| author | Abiola Owoyemi Ron Porat Amnon Lichter Adi Doron-Faigenboim Omri Jovani Noam Koenigstein Yael Salzer |
| author_facet | Abiola Owoyemi Ron Porat Amnon Lichter Adi Doron-Faigenboim Omri Jovani Noam Koenigstein Yael Salzer |
| author_sort | Abiola Owoyemi |
| collection | DOAJ |
| description | 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 (<i>p</i> < 0.001) fruit weight loss, firmness, decay, color, peel damage, chilling injury, internal dryness, acidity, vitamin C and ethanol levels, and flavor and acceptance scores. Four regression models were evaluated for their ability to predict fruit quality based on pre-harvest and postharvest features. Extreme gradient boosting (XGBoost) combined with a duplication approach was found to be the most effective approach. It allowed for the prediction of fruit-acceptance scores among the full data set, with a root mean square error (RMSE) of 0.217 and an R<sup>2</sup> of 0.891. |
| first_indexed | 2024-03-09T21:54:49Z |
| format | Article |
| id | doaj.art-688974e8e6144615833107d1fba1178c |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T21:54:49Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-688974e8e6144615833107d1fba1178c2023-11-23T20:00:03ZengMDPI AGFoods2304-81582022-06-011113184010.3390/foods11131840Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction ModelsAbiola Owoyemi0Ron Porat1Amnon Lichter2Adi Doron-Faigenboim3Omri Jovani4Noam Koenigstein5Yael Salzer6Department of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, IsraelDepartment of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, IsraelDepartment of Postharvest Science of Fresh Produce, ARO, The Volcani Institute, Rishon LeZion 7528809, IsraelGenomics and Bioinformatics Unit, ARO, The Volcani Institute, Rishon LeZion 7528809, IsraelDepartment of Industrial Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 6997801, IsraelDepartment of Industrial Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 6997801, IsraelDepartment of Growing, Production and Environmental Engineering, ARO, The Volcani Institute, Rishon LeZion 7528809, IsraelWe 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 (<i>p</i> < 0.001) fruit weight loss, firmness, decay, color, peel damage, chilling injury, internal dryness, acidity, vitamin C and ethanol levels, and flavor and acceptance scores. Four regression models were evaluated for their ability to predict fruit quality based on pre-harvest and postharvest features. Extreme gradient boosting (XGBoost) combined with a duplication approach was found to be the most effective approach. It allowed for the prediction of fruit-acceptance scores among the full data set, with a root mean square error (RMSE) of 0.217 and an R<sup>2</sup> of 0.891.https://www.mdpi.com/2304-8158/11/13/1840citrusintelligent logisticsmodelingorangepostharvest |
| spellingShingle | Abiola Owoyemi Ron Porat Amnon Lichter Adi Doron-Faigenboim Omri Jovani Noam Koenigstein Yael Salzer Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models Foods citrus intelligent logistics modeling orange postharvest |
| title | Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models |
| title_full | Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models |
| title_fullStr | Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models |
| title_full_unstemmed | Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models |
| title_short | Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models |
| title_sort | large scale high throughput phenotyping of the postharvest storage performance of rustenburg navel oranges and the development of shelf life prediction models |
| topic | citrus intelligent logistics modeling orange postharvest |
| url | https://www.mdpi.com/2304-8158/11/13/1840 |
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