Optimizing Recovery of High-Added-Value Compounds from Food
Author Information
Author(s): Liu Yixuan, Dar Basharat N., Makroo Hilal A., Aslam Raouf, Martí-Quijal Francisco J., Castagnini Juan M., Amigo Jose Manuel, Barba Francisco J.
Primary Institution: Research Group in Innovative Technologies for Sustainable Food, Universitat de València
Hypothesis
The study investigates the effectiveness of multivariate methods like RSM and ANNs in optimizing the recovery of high-added-value compounds in the food industry.
Conclusion
Combining RSM and ANNs can significantly enhance the recovery and quality of high-value compounds in food processing.
Supporting Evidence
- RSM is effective for modeling complex multivariate processes.
- ANNs excel in handling nonlinear relationships and large datasets.
- Combining RSM and ANNs offers a powerful approach to improve predictive models.
- Both methods support greener food processing by optimizing resources and reducing waste.
Takeaway
This study shows that using smart methods can help get more good stuff from food while wasting less.
Methodology
The review compares Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) for optimizing extraction processes.
Limitations
The study highlights the need for further exploration of scalability and integration with emerging technologies.
Digital Object Identifier (DOI)
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