MétaCan
Menu
Back to cohort
Record W4210623410 · doi:10.3390/molecules27031059

Physicochemical and Functional Properties of 2S, 7S, and 11S Enriched Hemp Seed Protein Fractions

2022· article· en· W4210623410 on OpenAlexafffund
Comfort F. Ajibola, Rotimi E. Aluko

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolubilityChemistryProtein isolateFood scienceIn vitroFraction (chemistry)Yield (engineering)ChromatographyBiochemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The hemp seed contains protein fractions that could serve as useful ingredients for food product development. However, utilization of hemp seed protein fractions in the food industry can only be successful if there is sufficient information on their levels and functional properties. Therefore, this work provides a comparative evaluation of the structural and functional properties of hemp seed protein isolate (HPI) and fractions that contain 2S, 7S, or 11S proteins. HPI and protein fractions were isolated at pH values of least solubility. Results showed that the dominant protein was 11S, with a yield of 72.70 ± 2.30%, while 7S and 2S had values of 1.29 ± 0.11% and 3.92 ± 0.15%, respectively. The 2S contained significantly (p < 0.05) higher contents of sulfhydryl groups at 3.69 µmol/g when compared to 7S (1.51 µmol/g), 11S (1.55 µmol/g), and HPI (1.97 µmol/g). The in vitro protein digestibility of the 2S (72.54 ± 0.52%) was significantly (p < 0.05) lower than those of the other isolated proteins. The intrinsic fluorescence showed that the 11S had a more rigid structure at pH 3.0, which was lost at higher pH values. We conclude that the 2S fraction has superior solubility, foaming capacity, and emulsifying activity when compared to the 7S, 11S, and HPI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.191
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueMoleculesSame topicProteins in Food SystemsFrench-language works237,207