Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning
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 rel...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/2/200 |
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| author | Józef Gorzelany Justyna Belcar Piotr Kuźniar Gniewko Niedbała Katarzyna Pentoś |
| author_facet | Józef Gorzelany Justyna Belcar Piotr Kuźniar Gniewko Niedbała Katarzyna Pentoś |
| author_sort | Józef Gorzelany |
| collection | DOAJ |
| description | 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<sup>−1</sup> to 1.42 g⋅100 g<sup>−1</sup>, 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. |
| first_indexed | 2024-03-09T22:53:48Z |
| format | Article |
| id | doaj.art-fb111feebf604bcbaa35608f831aecac |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T22:53:48Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-fb111feebf604bcbaa35608f831aecac2023-11-23T18:16:16ZengMDPI AGAgriculture2077-04722022-01-0112220010.3390/agriculture12020200Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine LearningJózef Gorzelany0Justyna Belcar1Piotr Kuźniar2Gniewko Niedbała3Katarzyna Pentoś4Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, PolandDepartment of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, PolandDepartment of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, PolandDepartment of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandInstitute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmonskiego Street, 51-630 Wrocław, PolandThe 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<sup>−1</sup> to 1.42 g⋅100 g<sup>−1</sup>, 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.https://www.mdpi.com/2077-0472/12/2/200large cranberrymechanical propertiescranberry compressionwater contentmathematical modellingmachine learning |
| spellingShingle | Józef Gorzelany Justyna Belcar Piotr Kuźniar Gniewko Niedbała Katarzyna Pentoś Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning Agriculture large cranberry mechanical properties cranberry compression water content mathematical modelling machine learning |
| title | Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning |
| title_full | Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning |
| title_fullStr | Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning |
| title_full_unstemmed | Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning |
| title_short | Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning |
| title_sort | modelling of mechanical properties of fresh and stored fruit of large cranberry using multiple linear regression and machine learning |
| topic | large cranberry mechanical properties cranberry compression water content mathematical modelling machine learning |
| url | https://www.mdpi.com/2077-0472/12/2/200 |
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