Preparation of Low-Phenylalanine Macro Peptides and Estimation of its Phenylalanine Content by Fluorometric Technique
Bibliographic record
Abstract
The aims of the study were to prepare macro peptides low in phenylalanine (Phe) from non-conventional raw materials, and to demonstrate the feasibility of using the fluorometric technique to measure the diminution of their Phe content. Aqueous solution of flours of legumes, and amaranth panicles were used to elaborate the concentrates by using isoelectric precipitation. These protein concentrates, and a whey solution were incubated with proteolytic enzymes to hydrolyze the peptide link at the aromatic amino acids, and then these macro peptides were filtrated through activated charcoal, in order to reduce its phenylalanine concentration. The Phe concentration, of the each prepared macro peptides, was analyzed by using fluorometric technique, and it was later validated by using HPLC. The crude protein contents in the concentrates have varied from 90% in the protein isolate from lentils, 76% in those from the frijol white, and 44% in those from amaranth panicles. Protein concentrates, and whey were hydrolyzed by using the following enzymes: pepsin from the pig gastric mucosa, protease from Aspergillus oryzae, and protease type XIV from Streptomyces griseus. It was determined that the enzymes with the better hydrolysis capacity, were the proteases from S. griseus and A. oryzae. The macro peptides with non-linked phe were filtered through activated charcoal. Reductions of Phe of up to 99% in the second and third filtrate were observed and this reduction was corroborated by using HPLC technique. It was also established the higher sensitivity of the fluorometric method to detect Phe, than the HPLC technique.
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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".