Интегральные гематологические показатели при механической желтухе
Bibliographic record
Abstract
In this study the potential of aqueous solvent washing on removing off-flavours in air classified pea protein-enriched flour (PPEF) was investigated. Unpleasant flavour compounds are one of the main deterrents to the application of pulses. PPEF was treated with ethanol or isopropanol at three different concentrations (20%, 50%, and 80%) to remove the volatiles related to unpleasant beany, earthy and astringent flavours. Headspace solid phase microextraction followed by GC-MS was used to identify the flavour compounds in untreated and treated PPEF. Besides the flavour profile, changes to their proximate composition, colour, functionality and protein quality were compared among untreated and treated samples. Higher concentrations of ethanol and isopropanol (50% and 80%) showed greater effectiveness in removing flavour compounds by reducing the total peak area by 82%-94%. Protein content in all treated samples (58.2%-64.3% d.b.) increased compared to untreated PPEF (55.5%) as a result of purification due to the decrease in ash, lipid and carbohydrate content. However, alcohol treatment reduced the protein solubility and oil holding capacity in all samples by 38.3%-75.9%, and 16.7%-30.2%, respectively. Although in vitro protein digestibility was improved with the solvent treatments, the amino acid scores of those samples became lower (i.e., reduced levels of methionine, cysteine or tryptophan) resulting in up to a 27.8% reduction in in vitro protein digestibility correct amino acid scores. Both ethanol and isopropanol at 50% and 80% concentration proved to be effective in removing flavour compounds in PPEF with some modifications on the chemical compositions, protein functionalities and quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".