Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods
Yixuan Liu,
Basharat N. Dar,
Hilal A. Makroo,
Raouf Aslam,
Francisco J. Martí-Quijal,
Juan M. Castagnini,
Jose Manuel Amigo,
Francisco J. Barba
Affiliations
Yixuan Liu
Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain
Basharat N. Dar
Department of Food Technology, Islamic University of Science and Technology, Awantipora 192122, Jammu & Kashmir, India
Hilal A. Makroo
Department of Food Technology, Islamic University of Science and Technology, Awantipora 192122, Jammu & Kashmir, India
Raouf Aslam
Department of Processing and Food Engineering, Punjab Agricultural University, Ludhiana 141004, Punjab, India
Francisco J. Martí-Quijal
Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain
Juan M. Castagnini
Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain
Jose Manuel Amigo
IKERBASQUE, Basque Society for the Promotion of Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
Francisco J. Barba
Research Group in Innovative Technologies for Sustainable Food (ALISOST), Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Spain
In today’s food industry, optimizing the recovery of high-value compounds is crucial for enhancing quality and yield. Multivariate methods like Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) play key roles in achieving this. This review compares their technical strengths and examines their sustainability impacts, highlighting how these methods support greener food processing by optimizing resources and reducing waste. RSM is valued for its structured approach to modeling complex processes, while ANNs excel in handling nonlinear relationships and large datasets. Combining RSM and ANNs offers a powerful, synergistic approach to improving predictive models, helping to preserve nutrients and extend shelf life. The review emphasizes the potential of RSM and ANNs to drive innovation and sustainability in the food industry, with further exploration needed for scalability and integration with emerging technologies.
