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

Józef Gorzelany, Justyna Belcar, Piotr Kuźniar, Gniewko Niedbała and Katarzyna Pentoś
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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

Agriculture, 2022, vol. 12, issue 2, 1-13

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.

Keywords: large cranberry; mechanical properties; cranberry compression; water content; mathematical modelling; machine learning (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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