From Waste to Plant‐Based Protein: Impact of Solid‐State Fermentation on Functionality and Protein Extraction of Cassava Leaves
Bibliographic record
Abstract
ABSTRACT Background and Objectives Cassava leaves represent a significant waste stream in cassava cultivation, currently underutilized in both food and nonfood applications. This study investigates the use of solid‐state fermentation with Aspergillus oryzae , Lactobacillus plantarum , and Bacillus subtilis to modify the composition and functional properties of cassava leaf flour (CLF), as well as to serve as a pretreatment for wet fractionation aimed at producing higher protein ingredients. Finds Results showed that fermentation increased the protein content of CLF while reducing anti‐nutritional compounds. Fermentation had no significant effect on water‐holding capacity or protein digestibility, but it increased oil‐holding capacity and decreased solubility. Alkaline extraction followed by isoelectric precipitation was applied to extract proteins from the fermented CLF; however, fermentation did not improve protein availability, as evidenced by the lower protein purity of the concentrates when compared to non‐fermented samples. Nevertheless, the functional properties of the protein concentrates obtained from the fermented cassava leaves were improved, demonstrating a positive effect of fermentation on the extracted protein's techno‐functional potential. Conclusions Overall, fermentation increased the protein content of CLF, reduced anti‐nutritional compounds, and enhanced the functional properties of the extracted protein. Significance and Novelty These findings suggest that fermentation of cassava leaf flour represents a viable biotechnological strategy for sustainable food industry applications, contributing to the development of more sustainable food supply chains.
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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 | 0.001 | 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".