Parvej Hasan Jon
Researcher
Also affiliated: Shahjalal University of Science and Technology (2024–2026)
Faculty Researcher
Food Science
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Parvej Hasan Jon's research focuses on applying advanced computational methods and novel extraction techniques to food science and agricultural challenges. He utilizes artificial neural networks (ANN) and response surface methodology (RSM) to optimize the extraction of bioactive compounds from plant-based sources, such as watermelon rinds and citrus lemon peels. His work also explores the development of biodegradable packaging films from agricultural byproducts like watermelon rind pectin and pineapple peel nanocellulose, investigating their characterization and food application potential.
Furthermore, Jon investigates the use of machine learning, specifically convolutional neural networks (CNN), for automated disease detection in crops, such as tea leaves. He has also applied machine learning to optimize edible coatings for extending the shelf life of fruits like strawberries. His scholarship metrics include an h-index of 7, with 14 total publications and 95 citations.
Metrics
- h-index: 7
- Publications: 14
- Citations: 104
Selected Publications
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Tribo-electrostatic separation of rice bran protein at distinctive pH: Where surface physics meets protein chemistry (2026)
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Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life (2025)
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Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN) (2025)
Collaboration Network
Top Collaborators
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Optimization of hybrid green extraction techniques for bioactive compounds from citrus lemon peel using response surface methodology (RSM) and artificial neural network (ANN)
- Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life
- Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life
- Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life
- Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life
- Machine learning-based optimization of alginate, guar gum, and pectin-based edible coatings for extended strawberry shelf life
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