Membrane Applications in Functional Foods and Nutraceuticals
Bibliographic record
Abstract
The functional foods and nutraceuticals market is growing at a rapid pace. Membrane processing offers several advantages over conventional methods for separation, fractionation, and recovery of those bioactive components. In this review, membrane applications of lipid-, carbohydrate-, and protein-based nutraceuticals and some minor bioactive components have been critically evaluated. Both non-porous and porous membranes were employed for lipid-based nutraceuticals separations. The use of non-porous membranes together with non-aqueous solvents brought about the impact of solution-diffusion theory on transport through membranes. Both organic and inorganic membranes gave encouraging results for the recovery of lipid components with single- and/or multi-stage membrane processing. Two-stage ultrafiltration (UF)-nanofiltration (NF) systems with polymeric membranes provided an efficient approach for the removal of high- and low-molecular weight (MW) unwanted components resulting in higher purity oligosaccharides in the NF retentate. The charged nature of protein-based nutraceutical components had a major effect on their separation. Operating at optimizal pH levels was critical for fractionation, especially for low MW peptide hydrolysates. Processing of minor components such as polyphenols, utilized all types of porous membranes from prefiltration to concentration stages. Coupling of membrane separation and supercritical fluid technologies would combine unique advantages of each process resulting in a novel separation technology offering great potential for the nutraceutical and functional food industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".