Impacts of Tempering and Infrared Heating of Pulse Seeds on the Functionality and Digestibility of Air‐Classified Fine Stream
Bibliographic record
Abstract
ABSTRACT The objective of this study was to evaluate the functional properties and in vitro protein digestibility (IVPD) of air‐classified fine stream (i.e., protein concentrates) obtained from round pea, faba bean, and wrinkled pea that were pre‐treated by tempering and subsequent infrared (IR) heating prior to air classification. Due to the absence of effective modifications on protein and starch, the tempering did not influence the separation between these two major constituents during the air classification and exhibited negligible effects on the functional attributes and digestibility of the obtained fine stream, except for water‐holding capacity (WHC). IR pre‐treatment led to protein denaturation and starch gelatinization in round pea, faba bean, and wrinkled pea, thus showing some negative influence on the separation of protein and starch in the air classification. Although IR heating reduced the protein solubility of fine stream from 79.9%–83.4% to 24.0%–33.1% for the three pulses, this pre‐treatment did not considerably affect their particle morphologies and size distributions nor foaming and emulsifying properties. As a result of protein denaturation, IR heating enhanced WHC from 0.42–0.65 g/g to 1.82–1.98 g/g and IVPD from 78.6%–83.0% to 85.3%–86.7% for the three fine stream samples. IR heating can be utilized to pre‐treat different pulse seeds to diversify the techno‐functional attributes and enhance the digestibility of protein concentrates obtained through air classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".