ArticleFoods (Basel, Switzerland)2022
Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of 'Rustenburg' Navel Oranges and the Development of Shelf-Life Prediction Models.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological quantification.Scientific reports · 2026Article
- A Systematic Review of Historical Temperature Data Use in Citrus Quality Assessment for Export Supply Chains.Foods (Basel, Switzerland) · 2026Review
- NMR metabolomics as a complementary tool to brix-acid tests for navel orange quality control of long-term cold storage.Scientific reports · 2024Article
- Building a physics-based virtual refrigerated container filled with fruit in ventilated packaging.MethodsX · 2024Article
- Identification of key sensory and chemical factors determining flavor quality of Xinyu mandarin during ripening and storage.Food chemistry: X · 2024Article
- Advances Postharvest Preservation Technology.Foods (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
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 < 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 R2 of 0.891.
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Registered trials
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