ArticleFood chemistry: X2025
Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system.
Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Computational intelligence applications in predicting energy consumption, greenhouse gas emissions, and drying performance of hybrid infrared dryer.Scientific reports · 2026Article
- Modeling and Optimization of Microwave Vacuum Drying for Pinelliae Rhizoma: Integrating Drying Kinetics, Artificial Neural Networks, and Quality Preservation.Food science & nutrition · 2025Article
- Artificial neural network and machine learning predictive model for assessing physicochemical properties of garlic slices (Food chemistry: X · 2025Article
- Predicting mass transfer activation energy and physicochemical properties of dried onion using numerical modeling and artificial intelligence.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.