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  5. Blueberry supply chain: Critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss
 
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Blueberry supply chain: Critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss

Author(s)
Ktenioudaki, Anastasia  
O'Donnell, C. P. (Colm P.)  
Emond, Jean Pierre  
Nascimento Nunes, Maria Cecilia do  
Uri
http://hdl.handle.net/10197/12876
Date Issued
2021-09
Date Available
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.
Sponsorship
European Commission Horizon 2020
Other Sponsorship
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
Subjects

Cold chain

Shelf-life

Machine learning

Biochemical propertie...

Post-harvest storage

DOI
10.1016/j.postharvbio.2021.111590
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by/3.0/ie/
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Revised_Manuscript_final.pdf

Size

880.61 KB

Format

Adobe PDF

Checksum (MD5)

24620e008e4a3f48835921dd482d07a2

Owning collection
Biosystems and Food Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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