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Record W3196468922 · doi:10.18280/ijdne.160411

Effect of Different Papain Concentrations on the Properties of Chicken Skin Protein Hydrolysates

2021· article· en· W3196468922 on OpenAlexvenueno aff
Ni Made Puspawati, Sri Wahjuni, Ni Kadek Inda Kusmaning Ayu, Ahmad Fudholi

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsnot available
FundersUniversitas Udayana
KeywordsPapainHydrolysateChemistryAntioxidantHydrolysisAmino acidChromatographyFood scienceAscorbic acidBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Chicken skin is a source of animal protein hydrolysate, which has a potential as an antioxidant. This study aimed to determine the effect of different concentrations of the enzyme papain on the degree of hydrolysis, antioxidant capacity, percentage of reducing power (%RP) and amino acid composition of chicken skin protein hydrolysate. Hydrolysis was carried out using papain with various concentrations (3%, 4% and 5%, w/w protein substrate) at pH 7 and 50℃ for 6 h. The degree of hydrolysis was determined by spectrophotometry. Antioxidant capacity and %RP was determined by ferric reducing antioxidant power method and expressed in milligram of ascorbic acid equivalent per gram of sample, and amino acid composition were determined using high-performance liquid chromatography. The results showed that variations in papain concentration had a significant effect (P<0.05) on the degree of hydrolysis, antioxidant capacity and %RP of chicken skin hydrolysate. The papain concentration of 4% resulted in the optimum protein hydrolysate with a degree of hydrolysis of 61.68%±0.64%, an antioxidant capacity of 8.72±0.30 mg AA/g sample and a %RP of 54.12%±1.78%. The protein hydrolysates of the treated chicken skin showed a high content of amino acids, namely, glycine, glutamate, proline, arginine and aspartate.

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 score0.014
Threshold uncertainty score0.089

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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