Repository logo
  • Log In
    New user? Click here to register.Have you forgotten your password?
University College Dublin
  • Colleges & Schools
  • Statistics
  • All of DSpace
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. College of Engineering & Architecture
  3. School of Biosystems and Food Engineering
  4. Biosystems and Food Engineering Research Collection
  5. Blueberry supply chain: Critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss
 
  • Details
Options

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

File(s)
FileDescriptionSizeFormat
Download Revised_Manuscript_final.pdf880.61 KB
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
September 2021
Date Available
10T16:07:37Z May 2022
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
Keywords
  • 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/
Owning collection
Biosystems and Food Engineering Research Collection
Scopus© citations
12
Acquisition Date
Jan 26, 2023
View Details
Views
252
Last Week
1
Acquisition Date
Jan 26, 2023
View Details
Downloads
40
Last Week
3
Acquisition Date
Jan 26, 2023
View Details
google-scholar
University College Dublin Research Repository UCD
The Library, University College Dublin, Belfield, Dublin 4
Phone: +353 (0)1 716 7583
Fax: +353 (0)1 283 7667
Email: mailto:research.repository@ucd.ie
Guide: http://libguides.ucd.ie/rru

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement