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Record W4401632766 · doi:10.22215/etd/2024-16130

Characterization of Oat Protein Fractions, Antioxidants and Tyrosinase Inhibitory Properties of their Hydrolysates

2024· dissertation· en· W4401632766 on OpenAlexaff
Barakat Koyinsola Azeez

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsCarleton University
Fundersnot available
KeywordsTyrosinaseHydrolysateInhibitory postsynaptic potentialChemistryAntioxidantBiochemistryEnzymeBiologyHydrolysis

Abstract

fetched live from OpenAlex

Oat bran is a byproduct of oat milling that has garnered significant attention for its nutritional and functional properties, particularly its protein content.Apart from the nutritional purposes of oat, bioactive peptides from different cereals and grains have been reported to have significant antioxidant and tyrosinase inhibitory properties This study aimed to characterize the protein fractions derived from oat bran, hydrolyze them with digestive enzyme, evaluate the antioxidant potential and tyrosinase inhibition of their hydrolysates.The protein fractions, albumin, glutelin and globulin, were sequentially extracted from oat bran.The protein content of each fraction was 83.8%, 53.1% and 67.7%, respectively.Subsequently, the protein fractions were analyzed for their physicochemical properties, molecular weight distribution, and functional properties using analytical techniques such as SDS-PAGE and mass spectrometry.The molecular characteristics from SDS-PAGE analysis under reducing conditions showed that all fractions content subunits of 12 globulin at about 20 and 35 kDa but with differences in concentrations and intensity.In addition, the albumin fraction had a band at 10 kDa while the glutelin fraction had 52 and 150 kDa bands.The protein hydrolysates were prepared using digestive enzymes pepsin and In the journey of completing this thesis, I am deeply grateful to the Almighty.It is through His grace that I have found the strength to overcome challenges, the wisdom to navigate uncertainties, and the perseverance to reach this significant milestone.I would like to thank my supervisor, Professor Apollinaire Tsopmo for his support, patience, guidance, and supervision throughout this project.Special thanks to my parents Abdul Lateef and Abiola, your sacrifices, both seen and unseen, have provided me with the opportunity to pursue my academic aspirations."Ẹyin ni Olórùn ìkéjì mí láyé.".I am eternally grateful for the unconditional love, guidance, and the funny WhatsApp messages.To my siblings, Baba, Bariu, Iman and MM, the entertaining chaos that accompanied my academic journey, your teasing

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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