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Abstract

In this study, selected mechanical properties of fruits of six varieties of Japanese quince (Chaenomeles japonica) were investigated. The influence of their storage time and the applied ozone at a concentration of 10 ppm for 15 and 30 min on water content, skin and flesh puncture force, deformation to puncture and puncture energy was determined. After 60 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 97.94% to 94.39%. No influence of the ozonation process on the change in water content in the fruits was noted. The tests showed a significant influence of ozonation and storage time on the increase in the punch puncture force of the skin and flesh, deformation and puncture energy of the fruits. In order to establish the relationship between storage conditions for various varieties and selected mechanical parameters, a novel machine learning method was employed. The best model accuracy was achieved for energy, with a MAPE of 10% and a coefficient of correlation (R) of 0.92 for the test data set. The best metamodels for force and deformation produced slightly higher MAPE (12% and 17%, respectively) and R of 0.72 and 0.88.

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1009240
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Title
The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits
Author
Gorzelany, Józef 1   VIAFID ORCID Logo  ; Kuźniar, Piotr 1   VIAFID ORCID Logo  ; Zardzewiały, Miłosz 1   VIAFID ORCID Logo  ; Pentoś, Katarzyna 2   VIAFID ORCID Logo  ; Murawski, Tadeusz 3 ; Wojciechowski, Wiesław 4 ; Kurek, Jarosław 5   VIAFID ORCID Logo 

 Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland; [email protected] (J.G.); [email protected] (P.K.); [email protected] (M.Z.) 
 Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 37b Chelmonskiego Street, 51-630 Wroclaw, Poland 
 Monika Murawska Farm, Nowa Prawda 10, 21-450 Stoczek Łukowski, Poland 
 Institute of Agroecology and Plant Production, Wroclaw University of Environmental and Life Sciences, Grunwaldzki Sq. 24A, 50-363 Wroclaw, Poland; [email protected] 
 Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; [email protected] 
Publication title
Volume
14
Issue
11
First page
1995
Publication year
2024
Publication date
2024
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20770472
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2024-11-06
Milestone dates
2024-10-11 (Received); 2024-11-05 (Accepted)
Publication history
 
 
   First posting date
06 Nov 2024
ProQuest document ID
3132823327
Document URL
https://www.proquest.com/scholarly-journals/use-machine-learning-assess-impact-ozonation/docview/3132823327/se-2?accountid=208611
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-11-26
Database
ProQuest One Academic