The emulsifying capacity and stability of potato proteins and peptides: A comprehensive review
Bibliographic record
Abstract
The potato has recently attracted more attention as a promising protein source. Potato proteins are commonly extracted from potato fruit juice, a byproduct of starch production. Potato proteins are characterized by superior techno-functional properties, such as water solubility, gel-forming, emulsifying, and foaming properties. However, commercially isolated potato proteins are often denatured, leading to a loss of these functionalities. Extensive research has explored the influence of different conditions and techniques on the emulsifying capacity and stability of potato proteins. However, there has been no comprehensive review of this topic yet. This paper aims to provide an in-depth overview of current research progress on the emulsifying capacity and stability of potato proteins and peptides, discussing research challenges and future perspectives. This paper discusses genetic diversity in potato proteins and various methods for extracting proteins from potatoes, including thermal and acid precipitation, salt precipitation, organic solvent precipitation, carboxymethyl cellulose complexation, chromatography, and membrane technology. It also covers enzymatic hydrolysis for producing potato-derived peptides and methods for identifying potato protein-derived emulsifying peptides. Furthermore, it reviews the influence of factors, such as physicochemical properties, environmental conditions, and food-processing techniques on the emulsifying capacity and stability of potato proteins and their derived peptides. Finally, it highlights chemical modifications, such as acylation, succinylation, phosphorylation, and glycation to enhance emulsifying capacity and stability. This review provides insight into future research directions for utilizing potato proteins as sustainable protein sources and high-value food emulsifiers, thereby contributing to adding value to the potato processing industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".