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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Józef Gorzelany & Piotr Kuźniar & Miłosz Zardzewiały & Katarzyna Pentoś & Tadeusz Murawski & Wiesław Wojciechowski & Jarosław Kurek, 2024. "The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits," Agriculture, MDPI, vol. 14(11), pages 1-14, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1995-:d:1515444
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    References listed on IDEAS

    as
    1. Natalia Matłok & Tomasz Piechowiak & Miłosz Zardzewiały & Bogdan Saletnik & Maciej Balawejder, 2024. "Continuous Ozonation Coupled with UV-C Irradiation for a Sustainable Post-Harvest Processing of Vaccinium macrocarpon Ait. Fruits to Reduce Storage Losses," Sustainability, MDPI, vol. 16(13), pages 1-12, June.
    2. Miłosz Zardzewiały & Natalia Matłok & Tomasz Piechowiak & Bogdan Saletnik & Maciej Balawejder & Józef Gorzelany, 2024. "Preliminary Tests of Tomato Plant Protection Method with Ozone Gas Fumigation Supported with Hydrogen Peroxide Solution and Its Effect on Some Fruit Parameters," Sustainability, MDPI, vol. 16(8), pages 1-12, April.
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