נגישות
menu      
Advanced Search
Syntax
Search...
Volcani treasures
About
Terms of use
Manage
Community:
אסיף מאגר המחקר החקלאי
Powered by ClearMash Solutions Ltd -
Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models
Year:
2022
Source of publication :
Foods
Authors :
Doron-Faigenboim, Adi
;
.
Lichter, Amnon
;
.
Porat, Ron
;
.
Salzer, Yael
;
.
Volume :
11
Co-Authors:

Abiola Owoyemi

Ron Porat

Amnon Lichter

Adi Doron-Faigenboim

Omri Jovani

Noam Koenigstein

 Yael Salzer

 

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
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.

Note:
Related Files :
intelligent logistics
modeling
orange
Postharvest
Show More
Related Content
More details
DOI :
10.3390/foods11131840
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
60274
Last updated date:
13/07/2022 12:58
Creation date:
13/07/2022 12:58
You may also be interested in
Scientific Publication
Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models
11

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

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.

Scientific Publication
You may also be interested in