ReviewAntioxidants (Basel, Switzerland)2024
Optimizing Recovery of High-Added-Value Compounds from Complex Food Matrices Using Multivariate Methods.
Review in Antioxidants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Extruded Pseudocereal Snacks Mathematical Modelling Approaches for Prediction and Optimisation: A Review.Foods (Basel, Switzerland) · 2026Review
- A Multivariate RSM-PLS Framework and HPLC Polyphenolic Profiling for Characterizing Distinct Extraction Signatures in Pressurized Liquid vs. Conventional Stirring Extraction of Asteraceae Species.Antioxidants (Basel, Switzerland) · 2026Article
- Article
- Seaweed bioactives: A comprehensive review of their nutritional, medicinal, and functional roles.iScience · 2026Review
- Data-Driven Optimization of Polyphenol Recovery and Antioxidant Capacity from Medicinal Herbs Using Chemometrics and HPLC Profiling for Functional Food Applications.International journal of molecular sciences · 2025Article
- Biofabrication, statistical optimization, and characterization of collagen nanoparticles synthesized via Streptomyces cell-free system for cancer therapy.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
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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.