The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits

Józef Gorzelany, Piotr Kuźniar, Miłosz Zardzewiały, Katarzyna Pentoś (), Tadeusz Murawski, Wiesław Wojciechowski and Jarosław Kurek
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Józef Gorzelany: Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland
Piotr Kuźniar: Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland
Miłosz Zardzewiały: Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland
Katarzyna Pentoś: Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 37b Chelmonskiego Street, 51-630 Wroclaw, Poland
Tadeusz Murawski: Monika Murawska Farm, Nowa Prawda 10, 21-450 Stoczek Łukowski, Poland
Wiesław Wojciechowski: Institute of Agroecology and Plant Production, Wroclaw University of Environmental and Life Sciences, Grunwaldzki Sq. 24A, 50-363 Wroclaw, Poland
Jarosław Kurek: Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland

Agriculture, 2024, vol. 14, issue 11, 1-14

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.

Keywords: ozone; quince fruit; mechanical properties; metamodeling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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