Nutritional properties of pea protein‐enriched flour treated with different proteases to varying degrees of hydrolysis
Bibliographic record
Abstract
Abstract Background and objectives Pulses are a good source of protein; however, they contain phytochemicals that can negatively affect the protein quality. Enzymatic hydrolysis could be a means to enhance the nutritional value of pea by improving the digestibility of the protein. To this aim, a commercial air‐classified pea protein‐enriched flour (PPEF) was hydrolyzed by different proteases (trypsin, Savinase, papain, and pepsin) to different degrees of hydrolysis (DH 2%–4% and 10%–12%) and levels of phytochemicals and in vitro protein digestibility corrected amino acid score were examined. Findings Protease treatments reduced the contents of condensed tannins and total phenolics, as well as trypsin inhibitor and chymotrypsin inhibitor activities. Hydrolysis at 10%–12% DH (degrees of hydrolysis) improved the amino acid score of the PPEF, but it was still limiting sulfur amino acids, whereas low levels of hydrolysis (DH 2%–4%) decreased the amino acid score. The in vitro protein digestibility score increased slightly following hydrolysis, with the greatest improvement in digestibility occurring with the papain treatment (DH 10%–12%). The in vitro protein digestibility corrected amino acid score with trypsin hydrolysis at DH 2%–4% was the lowest at 59.17%, whereas pepsin hydrolysis (10%–12% DH) gave the highest score (72.89%). Conclusions Specific hydrolysis treatments could be used to improve the overall nutrition of PPEF, enhancing the potential utilization of modified pea protein ingredients. Significance and novelty This study utilized four enzymes with different specificities resulting in PPEF hydrolysates with different protein quality even when the DH was the same. Increasing the nutritional quality of a PPEF may give it a competitive advantage over other pea ingredients.
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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 | 0.000 | 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 | 0.000 |
| 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".