A review of lentil (<scp><i>Lens culinaris</i></scp> Medik) value chain: Postharvest handling, processing, and processed products
Bibliographic record
Abstract
Abstract Lentils ( Lens culinaris Medik.) are grown worldwide in diverse agroecological regions with significant global production and trade. Since early 2000s, lentils production and consumption have been growing beyond its traditional areas of production and utilization, notably in USA, Canada, Australia, UK, and many European Union countries. Lentils are a rich source of protein, minerals, and many bioactive compounds. Therefore, lentil‐based products can offer a healthy food choice for all consumers, including those who are vegetarian or vegans, and/or looking for meat protein alternatives due to health and/or environmental concerns. In order to avail all the benefits that lentils offer, a quality maintenance approach is essential across value‐chain operations of postharvest handling, storage, and value‐added processing. In recent years, lentils have been used increasingly in a variety of value‐added products and cuisines in the developed countries. Different processing methods, for example, cooking, autoclaving, extrusion, baking, roasting, fermentation, and sprouting, significantly improve protein bioavailability, total digestibility, and overall nutritional and organoleptic quality. A number of traditional and innovative processing techniques also have been used to produce lentil‐based end‐products or ingredients for various food applications. Overall, lentils are well positioned as a food legume crop to cater to emerging trends among consumers, especially those looking for healthy food choices, an alternative plant‐based protein for global food security, and foods that are produced in environmentally friendly and agriculturally sustainable manner. Significant production and consumption trends for lentils clearly demonstrate enhanced value for consumers and further impact in contributions to a nutritious global food supply.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.006 | 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".