Impacts of tempering and infrared heating of pulse seeds on the functionality and digestibility of air-classified fine stream
Bibliographic record
Abstract
This study aimed to employ tempering and subsequent infrared (IR) heating as seed pre-treatments to enhance the functional properties and in vitro protein digestibility (IVPD) of air-classified fine stream (i.e., protein concentrate) from round pea, faba bean, and wrinkled pea. 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). As an efficient and short-time thermal method, 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 pulse varieties, 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 improve the techno-functional attributes and digestibility of protein concentrates from air classification.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".