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Influence of the Processing on Composition, Protein Structure, and Techno-Functional Properties of Pork Byproduct Isolates Produced by Isoelectric Precipitation and Ultrafiltration–Diafiltration

2025· article· en· W4415169848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Food Science & Technology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsoelectric pointSolubilityPrecipitationDenaturation (fissile materials)Protein precipitationVolume (thermodynamics)

Abstract

fetched live from OpenAlex

The valorization of pork slaughterhouse byproducts presents a promising strategy to reduce the substantial volume of waste generated by the meat industry. In this work, isoelectric precipitation (IEP) and ultrafiltration–diafiltration (UF-DF) were used to produce protein isolates from pork byproducts (PBPI). Both isolates had comparable protein contents (81.73–84.56%) and similar fatty acid profiles, but PBPI-UFDF exhibited better nutritional lipid indices. The amino acid profiles of both isolates exceeded the FAO recommendations. Structural analyses revealed that UF-DF better preserved protein structure, with higher surface hydrophobicity (538.04 vs 77.96 AU), free sulfhydryl content (60.14 vs 16.44 μmol/g), and denaturation temperature (146.04 vs 106.10 °C) compared to IEP. This translated into improved protein solubility across a broad range of pH range (40–95% vs 3–37%) and lower protein aggregation. PBPI-UFDF also showed superior gelation properties, highlighting its potential as a high-quality functional protein ingredient.

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.

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 score0.018
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.196
Teacher spread0.186 · 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