Studies comparing the effectiveness of models for drying bitter gourd slices
Drying is an essential food preservation method, improving product shelf life and quality while reducing transportation and storage costs. This study evaluated the drying kinetics of bitter gourd slices under halogen drying conditions using both traditional empirical models (Page, Midilli, Logarithm...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2025-06-01
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| Series: | Czech Journal of Food Sciences |
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| Online Access: | https://cjfs.agriculturejournals.cz/artkey/cjf-202503-0004_studies-comparing-the-effectiveness-of-models-for-drying-bitter-gourd-slices.php |
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| _version_ | 1849418179984490496 |
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| author | Dinh Anh Tuan Tran Tuan Nguyen Van Thi Khanh Phuong Ho |
| author_facet | Dinh Anh Tuan Tran Tuan Nguyen Van Thi Khanh Phuong Ho |
| author_sort | Dinh Anh Tuan Tran |
| collection | DOAJ |
| description | Drying is an essential food preservation method, improving product shelf life and quality while reducing transportation and storage costs. This study evaluated the drying kinetics of bitter gourd slices under halogen drying conditions using both traditional empirical models (Page, Midilli, Logarithmic, Peleg, and Two-Term) and the machine learning-based random forest (RF) model. Experiments were conducted at 60 °C, 65 °C, and 70 °C with slice thicknesses of 3, 5, and 7 mm. Model performance was assessed using the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results show that the RF model demonstrated the highest accuracy, with an average R2 of 0.9826, the lowest RMSE (0.0655), and MAPE (1.40 %). Its ability to capture non-linear drying behaviour made it the most reliable model. The Midilli model was the best-performing traditional model, with an average R2 of 0.9851, but its accuracy declined for thicker slices and higher temperatures. Logarithmic and Peleg models exhibited significant errors, particularly during the mid-to-late drying phases. The results highlight RF's robustness and adaptability, outperforming traditional models in handling complex drying dynamics. |
| format | Article |
| id | doaj-art-2c25d7252058464aba1efafedc6d59ff |
| institution | Kabale University |
| issn | 1212-1800 1805-9317 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Czech Journal of Food Sciences |
| spelling | doaj-art-2c25d7252058464aba1efafedc6d59ff2025-08-20T03:32:31ZengCzech Academy of Agricultural SciencesCzech Journal of Food Sciences1212-18001805-93172025-06-0143320521510.17221/255/2024-CJFScjf-202503-0004Studies comparing the effectiveness of models for drying bitter gourd slicesDinh Anh Tuan Tran0https://orcid.org/0000-0002-7860-845XTuan Nguyen Van1Thi Khanh Phuong Ho2Faculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, VietnamDrying is an essential food preservation method, improving product shelf life and quality while reducing transportation and storage costs. This study evaluated the drying kinetics of bitter gourd slices under halogen drying conditions using both traditional empirical models (Page, Midilli, Logarithmic, Peleg, and Two-Term) and the machine learning-based random forest (RF) model. Experiments were conducted at 60 °C, 65 °C, and 70 °C with slice thicknesses of 3, 5, and 7 mm. Model performance was assessed using the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results show that the RF model demonstrated the highest accuracy, with an average R2 of 0.9826, the lowest RMSE (0.0655), and MAPE (1.40 %). Its ability to capture non-linear drying behaviour made it the most reliable model. The Midilli model was the best-performing traditional model, with an average R2 of 0.9851, but its accuracy declined for thicker slices and higher temperatures. Logarithmic and Peleg models exhibited significant errors, particularly during the mid-to-late drying phases. The results highlight RF's robustness and adaptability, outperforming traditional models in handling complex drying dynamics.https://cjfs.agriculturejournals.cz/artkey/cjf-202503-0004_studies-comparing-the-effectiveness-of-models-for-drying-bitter-gourd-slices.phprandom forest modelmomordica charantialtraditional drying modelhalogen dryermoisture content |
| spellingShingle | Dinh Anh Tuan Tran Tuan Nguyen Van Thi Khanh Phuong Ho Studies comparing the effectiveness of models for drying bitter gourd slices Czech Journal of Food Sciences random forest model momordica charantial traditional drying model halogen dryer moisture content |
| title | Studies comparing the effectiveness of models for drying bitter gourd slices |
| title_full | Studies comparing the effectiveness of models for drying bitter gourd slices |
| title_fullStr | Studies comparing the effectiveness of models for drying bitter gourd slices |
| title_full_unstemmed | Studies comparing the effectiveness of models for drying bitter gourd slices |
| title_short | Studies comparing the effectiveness of models for drying bitter gourd slices |
| title_sort | studies comparing the effectiveness of models for drying bitter gourd slices |
| topic | random forest model momordica charantial traditional drying model halogen dryer moisture content |
| url | https://cjfs.agriculturejournals.cz/artkey/cjf-202503-0004_studies-comparing-the-effectiveness-of-models-for-drying-bitter-gourd-slices.php |
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