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Review
. 2024 Dec 11;13(12):1510.
doi: 10.3390/antiox13121510.

Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods

Yixuan Liu  1 , Basharat N Dar  2 , Hilal A Makroo  2 , Raouf Aslam  3 , Francisco J Martí-Quijal  1 , Juan M Castagnini  1 , Jose Manuel Amigo  4   5 , Francisco J Barba  1
Affiliations
Review

Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods

Yixuan Liu et al. Antioxidants (Basel). .

Abstract

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.

Keywords: ANN; RSM; food industry; high-added-value compounds; optimization.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of RSM (Created in BioRender.com).
Figure 2
Figure 2
Flow chart of ANN (Created in BioRender.com).
Figure 3
Figure 3
Recovery of high-added-value components from the food industry using the ANNs or RSM optimization method (Created with BioRender.com).
Figure 4
Figure 4
Application of Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) for conventional and innovative extraction approaches (Created with BioRender.com).
See this image and copyright information in PMC

References

    1. Guiné R.P.F., Dets C. The use of artificial neural networks (ANN) in food process engineering. ETP Int. J. Food Eng. 2019;5:15–21. doi: 10.18178/ijfe.5.1.15-21. - DOI
    1. da Costa N.L., da Costa M.S., Barbosa R. A review on the application of chemometrics and machine learning algorithms to evaluate beer authentication. Food Anal. Methods. 2021;14:136–155. doi: 10.1007/s12161-020-01864-7. - DOI
    1. Junttila M.H. Extraction of brewers’ spent grain in near subcritical conditions: A method to obtain high protein contents extracts. J. Agric. Food Res. 2022;10:100378. doi: 10.1016/j.jafr.2022.100378. - DOI
    1. Aung T., Kim S.-J., Eun J.-B. A hybrid RSM-ANN-GA approach on optimisation of extraction conditions for bioactive component-rich laver (Porphyra dentata) extract. Food Chem. 2022;366:130689. doi: 10.1016/j.foodchem.2021.130689. - DOI - PubMed
    1. Yahya H.S.M., Abbas T., Amin N.A.S. Optimization of hydrogen production via toluene steam reforming over Ni–Co supported modified-activated carbon using ANN coupled GA and RSM. Int. J. Hydrogen Energy. 2021;46:24632–24651. doi: 10.1016/j.ijhydene.2020.05.033. - DOI

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