Blueberry supply chain: Critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss
|Title:||Blueberry supply chain: Critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss||Authors:||Ktenioudaki, Anastasia; O'Donnell, C. P. (Colm P.); Emond, Jean Pierre; Nascimento Nunes, Maria Cecilia do||Permanent link:||http://hdl.handle.net/10197/12876||Date:||Sep-2021||Online since:||2022-05-10T16:07:37Z||Abstract:||Blueberries have increased in popularity in recent years due to their nutritional benefits and sensory characteristics. However, to preserve quality and extend shelf-life, they need to be maintained at refrigerated temperatures and high relative humidity, conditions that are not routinely met along the supply chain. Poor temperature management leads to quality deterioration, increasing waste/losses along the supply chain. This study examined the impact of each step along the supply chain on the physichochemical quality and shelf-life of blueberries, identifying the most critical steps from field to consumption. The following steps were identified as critical in the blueberry supply chain: shipping to distribution centre (DC) (72 h at 5 °C), store display (48 h at 15 °C), and consumer (48 h at 20 °C). Given the economic importance of weight loss and its link to fruit quality and shelf-life, a boosted regression tree (BRT) model was built to predict weight loss using the post-harvest environmental conditions of a simulated supply chain applying different temperature-time scenarios. The model explained 84 % of the variance on the test set and highlighted the interactions of supply chain conditions on weight loss.||Funding Details:||European Commission Horizon 2020||Funding Details:||US Department of Agriculture (USDA)||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Postharvest Biology and Technology||Volume:||179||Start page:||1||End page:||10||Copyright (published version):||2021 the Authors||Keywords:||Cold chain; Shelf-life; Machine learning; Biochemical properties; Post-harvest storage||DOI:||10.1016/j.postharvbio.2021.111590||Language:||en||Status of Item:||Peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by/3.0/ie/|
|Appears in Collections:||Biosystems and Food Engineering Research Collection|
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