An integrative framework for eco-efficiency assessment in plant-protein extraction processes: Hotspot analysis, protein loss tracking, and uncertainty analysis
Bibliographic record
Abstract
Over the past few years, the growing demand for plant-based proteins has intensified the need to scale up extraction technologies, for efficient and sustainable protein production. Towards the adoption of existing methods, modified extraction methods as well as emerging technologies, there is the need to assess and ascertain the overall sustainability performance of the methods, integrating economic, product quality and environmental perspectives. However, a comprehensive assessment framework that integrates these three (3) sustinability dimensions, for the plant-protein industry, remains lacking. This study proposes a novel eco-efficiency assessment framework tailored to evaluate sustainability of plant-protein extraction methods, with a focus on industrial commercialization potential. The framework integrates eco-efficiency analysis, hotspot identification of economic value, protein loss tracking, protein purity, environmental impacts, and uncertainty and global sensitivity analyses to highlight key production variables that influences sustainability outcomes. The framework was applied to a case study of pea protein extraction by the alkaline-isoelectric precipitation (ALK-IEP) method with multiple trials using different yellow pea varieties. Results highlighted the major contribution of the spray drying, pasteurization and extraction-decantation sections to eco-environmental outcomes and protein content loss. The findings offer insights for optimizing the extraction process and improving eco-efficiency, contributing to more sustainable, scalable commercial pea protein production. • A novel holistic eco-efficiency assessment framework is proposed for evaluating plant-protein extraction methods. • The framework incorporates hotspot and global sensitivity analyses to reveal key variables that impact techno-eco-environmental performance. • The framework is applied to a case study on pea protein extraction by the wet fractionation method. • The findings provide valuable insights for improving eco-efficiency, and supporting the sustainable commercialization of pea protein production.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".