Extraction of protein and carbohydrates from soybean meal using acidic and alkaline solutions produced by electro‐activation
Bibliographic record
Abstract
Abstract This main objective of this work was to study the extraction of proteins and soluble carbohydrates and minerals from soybean meal by using acidic (anolyte) and alkaline (catholyte) extracting solutions produced by electro‐activation technology. The extracting solutions (anolyte and catholyte) were produced by electro‐activation at current intensities of 150, 300, and 450 mA during 10, 30, and 50 min. Both extraction methods were effective in extracting soluble dry matter. The catholyte was the most efficient in extracting proteins with a maximal yield of 45.55 ± 2.77%. Moreover, protein content increased with treatment time in catholyte extracts but decreased in anolyte‐extracted samples. SDS‐PAGE confirmed catholyte effectiveness in extracting more proteins with better quality than the anolyte. Similar results were observed for amino acid profiles, with samples extracted by the catholyte being the richest in both essential and nonessential amino acids. While the anolyte extracted the highest amounts of Mg2+ (78%), Ca2+ (37%), and P3− (37%) from the soybean meal, catholyte was more efficient in extracting Zn2+ (65%), Fe2+ (62%), and Cu2+ (42%) ions. The obtained samples by both anolytes and catholytes exhibited good carbohydrate profiles containing some prebiotic monosaccharides. The carbohydrate profile varied with voltage used for the production of electro‐activated solutions and time. Stachyose and raffinose content increased with voltage used for anolytes production while decreasing in catholytes samples. Similarly, the highest stachyose and raffinose contents were observed for catholyte‐treated samples for 10 min, with respective ranges of 206.64–222.49 and 31.17–34.29 mg/g. To a lesser extent, anolyte‐treated samples for a longer time (30–50 min) were more efficient in extracting those sugars.
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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) | 0.000 | 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 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".