Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 363

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Applications of mixed polysaccharide-protein systems in fabricating multi-structures of binary food gels—A review

Yang, Xi, Li, Anqi, Li, Dan, Guo, Yurong, Sun, Lijun·2021·Trends in Food Science & Technology

Abstract

Abstract Background Due to high water content, low calorie and appealing textural characteristics, gel-related foods have recently obtained great interest in the food industry. Compared with the same class of biopolymer mixtures (e.g., polysaccharide-polysaccharide mixtures and protein-protein mixtures), the biopolymer mixtures from different classes often possess more advantages in fabricating binary food gels. By combination of physical properties of polysaccharides and proteins, it is possible to produce multi- or hierarchical microstructures of gels. However, the fundamental principles of fabricating food gels based on mixtures of polysaccharides and proteins need to be further shed light on. Scope and approach In this review, the phase behaviors of mixed polysaccharide-protein systems were discussed, and their relationships with the microstructures of final gels were summarized. Then, the practical applications of polysaccharide-protein mixtures in producing composite binary gels were exemplified. Key Findings and Conclusions Intermolecular interactions are the predominant factors affecting the phase behaviors between polysaccharides and proteins, which can be regulated by altering some parameters, such as polymer concentration, pH, ionic strength, temperature, etc. The microstructures of final gels are highly dependent on the existing states of both polymers at the time of gelation, rather than their phase equilibrium states. Moreover, the physical properties and gel structures can be further diversified by changing the gel fabrication conditions. Therefore, it is suggested to rationally select polysaccharide-protein mixtures, as well as gel fabrication conditions to attain binary gels with expected properties.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.tifs.2021.01.002

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

Metadata

Authors
Yang, X., Li, A., Li, D., Guo, Y., & Sun, L.
Venue
Trends in Food Science & Technology
Publisher
Elsevier BV
Published
2021-03
Volume
109
Pages
197-210
ISSN
0924-2244 (ISSN-L)
OpenAlex
W3122437424
Cited by
363
References
3
FWCI
28.24
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.