Subcritical hydrolysis and characterization of waste proteinaceous biomass for value added applications
Bibliographic record
Abstract
Abstract BACKGROUND As a result of the bovine spongiform encephalopathy (BSE) emergence, certain tissues of cattle are categorized as specified risk material (SRM) and completely banned from their traditional applications as an ingredient in animal feed, pet food, or fertilizer applications. The goal of this study was to investigate the hydrolysis of such hazardous proteinacious biomass and extract a safe proteinacious fraction to produce industrial feedstock for value‐added applications. RESULTS The SRM was hydrolyzed at subcritical temperatures of 180, 200, 220, 240, and 260 °C for 40 min according to government‐approved protocols. The recovery and cleavage of proteins and lipids, the generation and degradation of free and total amino acids, and the generation of organic acids were studied and found to be temperature‐dependent. The yield of the hydrolyzed and recovered proteinacious fractions varied between 71.6 and 87.6% by weight of the original protein content in the SRM. The molecular size was also significantly dependent on the hydrolysis temperature. CONCLUSION The valorization of an otherwise waste SRM by subcritical hydrolysis and recovery techniques resulted in protein hydrolyzates suitable for bio‐based chemicals and materials applications. The increase of temperature resulted in higher degrees of hydrolysis as shown by the smaller molecular size of hydrolyzed proteins. © 2014 Society of Chemical Industry
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".