Utjecaj fermentacije na probavljivost proteina i udjel nenutritivnih sastojaka u koncentratu proteina graška
Bibliographic record
Abstract
In order to determine the impact of fermentation on protein quality, pea protein concentrate (PPC) was fermented with Lactobacillus plantarum for 11 h and total phenol and tannin contents, protease inhibitor activity, amino acid composition and in vitro protein digestibility were analyzed.Phenol levels, expressed as catechin equivalents (CE), increased on dry mass basis from 2.5 at 0 h to 4.9 mg CE per 1 g of PPC at 11 h.Tannin content rose from 0.14 at 0 h to a maximum of 0.96 mg CE per 1 g of PPC after 5 h, and thereafter declined to 0.79 mg/g after 11 h.After 9 h of fermentation trypsin inhibitor activity decreased, however, at all other fermentation times similar levels to the PPC at time 0 h were produced.Chymotrypsin inhibitor activity decreased from 3.7 to 1.1 chymotrypsin inhibitory units (CIU) per mg following 11 h of fermentation.Protein digestibility reached a maximum (87.4 %) after 5 h of fermentation, however, the sulfur amino acid score was reduced from 0.84 at 0 h to 0.66 at 11 h.This reduction in sulfur content altered the in vitro protein digestibility-corrected amino acid score from 67.0 % at 0 h to 54.6 % at 11 h.These data suggest that while fermentation is a viable method of reducing certain non-nutritive compounds in pea protein concentrate, selection of an alternative bacterium which metabolises sulfur amino acids to a lesser extent than L. plantarum should be considered.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".