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Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

Author

Listed:
  • Józef Gorzelany

    (Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland)

  • Justyna Belcar

    (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)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Katarzyna Pentoś

    (Institute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmonskiego Street, 51-630 Wrocław, Poland)

Abstract

The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g⋅100 g −1 to 1.42 g⋅100 g −1 , and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression.

Suggested Citation

  • Józef Gorzelany & Justyna Belcar & Piotr Kuźniar & Gniewko Niedbała & Katarzyna Pentoś, 2022. "Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning," Agriculture, MDPI, vol. 12(2), pages 1-13, January.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:200-:d:739729
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    Citations

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    Cited by:

    1. Gniewko Niedbała & Jarosław Kurek & Bartosz Świderski & Tomasz Wojciechowski & Izabella Antoniuk & Krzysztof Bobran, 2022. "Prediction of Blueberry ( Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods," Agriculture, MDPI, vol. 12(12), pages 1-27, December.
    2. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    3. Piotr Boniecki & Agnieszka Sujak & Gniewko Niedbała & Hanna Piekarska-Boniecka & Agnieszka Wawrzyniak & Andrzej Przybylak, 2023. "Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    4. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    5. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.

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