Innovative strategies for valorization of byproducts from soybean industry: A review on status, challenges, and sustainable approaches towards zero-waste processing systems
Bibliographic record
Abstract
The agro-food supply chain generates significant quantities of waste and byproducts globally, influenced by regional socioeconomic conditions, policy frameworks, and environmental concerns. The soybean industry generates various byproducts during the production processes of oil, soy milk, tofu, soy yogurt, edamame, miso, tempeh, natto, and soy sauce, presenting both challenges and opportunities for sustainable valorization. The review aims to outline the composition, status, and potential applications of key byproducts within the soybean industry including soy okara, soy whey, soy hull, soy meal, and lecithin, elucidating innovative strategies for their comprehensive valorization. The goal is to establish a sustainable zero-waste processing system by effectively utilizing these byproducts. This review explores emerging biotechnological advancements and eco-friendly processes aimed at maximizing resource recovery through the valorization of these soy byproducts. Various commercially viable products derived from repurposing the carbohydrate and protein fractions of diverse soy byproducts are highlighted. Additionally, a cutting-edge framework is proposed, advocating for the establishment of a zero-waste system within the soybean processing sector, emphasizing integrated biorefinery technologies, circular economy strategies, and sustainability principles. The framework proposed encompasses maximizing okara utilization, extracting value-added products, and implementing a closed-loop byproduct management approach within collaborative supply chains. Despite promising prospects, challenges such as anti-nutrients, viscosity and solubility of soy powder, and environmental impact must be addressed. This study could inspire further research into innovative technologies for the comprehensive and integrated valorization of soy byproducts, aiming to mitigate food waste and enhance the sustainability of the soybean 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".