Processing of Amaranthus hypochondriacus Biomass for Functional Protein Concentrates Development
Bibliographic record
Abstract
Most proteins for human consumption are animal-derivated sources; however, due to its environmental impact (soil erosion, gas emissions, water footprint) and increasing demand, different plant-based sources are necessary. The purpose of this work is to use amaranth crop biomass for protein concentrates production. The effect of processing (extraction and drying) on the structure and some functional properties of biomass protein concentrates are evaluated for food applications. Thermal and acid precipitation was used for extraction of soluble proteins and lyophilization, vacuum convection, and spray-drying methods for protein concentrates production. Protein structure analyses were gel electrophoresis (SDS-PAGE), isoelectric point (zeta potential), spectrometry (FTIR), calorimetry (DSC), and amino acids profile. The functionality was evaluated by solubility (UV-VIS), rheological and emulsifying properties at different pH values. The most preserved protein structure was the precipitated by acidification and dried by spraying, which showed the highest solubility values (> 90 %), and desired rheological properties. Lyophilization enhanced the emulsifying activity (~95 %) and stability to gravity (~63 %) and heat (~79 %). The essential amino acids content (49 %) was higher than FAO recommendations for children (36 %), and protein composition was in agreement with the Codex Alimentarius for plant protein consumption. This research approaches the use of highly available biomass from amaranth crop as a sustainable source of proteins. Mostly protein isolation uses expensive technologies, which increases the final product cost. Herein, a simple method was adequate to obtain high nutritional quality, and food functionality protein concentrates for food-related applications.
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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 | 0.000 | 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) | 0.001 | 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".