Techno-eco-environmental assessment of dry fractionation of protein concentrates from yellow peas with different pre-treatment methods: An eco-efficiency approach
Bibliographic record
Abstract
For holistic and concise decision support, it is essential to assess the sustainability of pea protein production pathways from the technical, economic, and environmental perspectives. Although regarded as the most sustainable protein extraction process, sustainability assessment of different dry fractionation pathways has not yet been carried out. To address this limitation, this study carried out a comparative techno-eco-environmental assessment of three different dry fractionation scenarios, a baseline, and two pathways with upstream pre-treatment methods of Radio Frequency (RF) treatment and Infrared Radiation (IR) treatment. Process Separation Efficiency (PSE), Life Cycle Assessment (LCA), and Techno-economic Assessment (TEA) were performed. Findings from the study showed that the RF-treated and IR-treated pea seeds produce higher yields of protein concentrates with higher protein content, as compared to the baseline. However, the higher protein separation efficiency could not comparatively offset the capital costs, processing costs, and higher energy demand associated, causing it to be outperformed by the baseline, in the economic and environmental dimensions. Although the IR treatment pathway performed better than RF treatment environmentally, it performed the least at the economic criteria. Overall, performance levels carried out using economic and protein quality value improvement for eco-efficiency assessment showed that it is very salient to consider all these three criteria integratively when assessing the sustainability performance of protein extraction pathways to identify trade-offs amongst the different dimensions. Moreover, competitive advantage played a key role in the eco-efficiency performance levels. We therefore recommend that further studies to be conducted including the product techno-functionality for a broader and more holistic perspective.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".