Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system
This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM)....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Food Chemistry: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157525000951 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856691065651200 |
|---|---|
| author | Hany S. El-Mesery Ahmed H. ElMesiry Evans K. Quaye Zicheng Hu Ali Salem |
| author_facet | Hany S. El-Mesery Ahmed H. ElMesiry Evans K. Quaye Zicheng Hu Ali Salem |
| author_sort | Hany S. El-Mesery |
| collection | DOAJ |
| description | This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM). The results showed that higher infrared intensity and air temperature improved rehydration and colour but reduced water activity and vitamin C levels. In contrast, faster airflow helped preserve quality by counteracting the negative effects of higher temperatures and infrared intensity. The SOM algorithm identified five optimal drying conditions, revealing that lower temperatures, lower infrared intensity, and higher airflow provided optimal conditions for improving the quality of okra slices. Interestingly, the machine learning model's predictions closely matched the test data sets, providing valuable insights for understanding and controlling the factors affecting okra drying performances. This study used machine learning to optimize the drying process of okra, a new approach for improving food drying techniques. It offers valuable insights for the food industry in its quest to improve efficiency without sacrificing product quality. |
| format | Article |
| id | doaj-art-b1b622584a3b489ebb4411104f110936 |
| institution | Kabale University |
| issn | 2590-1575 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj-art-b1b622584a3b489ebb4411104f1109362025-02-12T05:32:43ZengElsevierFood Chemistry: X2590-15752025-01-0125102248Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating systemHany S. El-Mesery0Ahmed H. ElMesiry1Evans K. Quaye2Zicheng Hu3Ali Salem4School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China; Agricultural Engineering Research Institute, Agricultural Research Center, Dokki 12611, Giza, EgyptFaculty of Computer Science and Engineering, New Mansoura University, 35742, EgyptSchool of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China; Corresponding author at: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China.Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Pécs 7622, Hungary; Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt; Corresponding author at: Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pecs, Hungary.This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM). The results showed that higher infrared intensity and air temperature improved rehydration and colour but reduced water activity and vitamin C levels. In contrast, faster airflow helped preserve quality by counteracting the negative effects of higher temperatures and infrared intensity. The SOM algorithm identified five optimal drying conditions, revealing that lower temperatures, lower infrared intensity, and higher airflow provided optimal conditions for improving the quality of okra slices. Interestingly, the machine learning model's predictions closely matched the test data sets, providing valuable insights for understanding and controlling the factors affecting okra drying performances. This study used machine learning to optimize the drying process of okra, a new approach for improving food drying techniques. It offers valuable insights for the food industry in its quest to improve efficiency without sacrificing product quality.http://www.sciencedirect.com/science/article/pii/S2590157525000951Infrared drying of OkraOptimizationMachine Learning in Food ProcessingSelf-Organizing Mapphysicochemical properties |
| spellingShingle | Hany S. El-Mesery Ahmed H. ElMesiry Evans K. Quaye Zicheng Hu Ali Salem Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system Food Chemistry: X Infrared drying of Okra Optimization Machine Learning in Food Processing Self-Organizing Map physicochemical properties |
| title | Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| title_full | Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| title_fullStr | Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| title_full_unstemmed | Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| title_short | Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| title_sort | machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system |
| topic | Infrared drying of Okra Optimization Machine Learning in Food Processing Self-Organizing Map physicochemical properties |
| url | http://www.sciencedirect.com/science/article/pii/S2590157525000951 |
| work_keys_str_mv | AT hanyselmesery machinelearningalgorithmforestimatingandoptimizingthephytochemicalcontentandphysicochemicalpropertiesofokraslicesinaninfraredheatingsystem AT ahmedhelmesiry machinelearningalgorithmforestimatingandoptimizingthephytochemicalcontentandphysicochemicalpropertiesofokraslicesinaninfraredheatingsystem AT evanskquaye machinelearningalgorithmforestimatingandoptimizingthephytochemicalcontentandphysicochemicalpropertiesofokraslicesinaninfraredheatingsystem AT zichenghu machinelearningalgorithmforestimatingandoptimizingthephytochemicalcontentandphysicochemicalpropertiesofokraslicesinaninfraredheatingsystem AT alisalem machinelearningalgorithmforestimatingandoptimizingthephytochemicalcontentandphysicochemicalpropertiesofokraslicesinaninfraredheatingsystem |