Lentil (<scp><i>Lens culinaris</i></scp>Medik) as nutrient‐rich and versatile food legume: A review
Bibliographic record
Abstract
Abstract Lentil is one of the most important food legumes consumed widely throughout the world. Lentils are produced in diverse agroecological regions, such as Asia, North and South America, Africa, and Oceania. During the last two decades (2001–2020), world production of lentils increased by 107%, from 3.15 to 6.54 million metric tons. Canada leads lentil producing countries (with 44% share of the global output), followed by India and Australia having 18% and 8% share, respectively. Being a rich source of protein, complex carbohydrates, dietary fiber, and folic acid, lentils are considered a healthy food nutritionally. Lentils also contain a number of bioactive phytochemicals, such as flavonoids, total phenolics, phytate, saponins, and tannins. Dehulling and splitting of lentils are the most‐commonly used basic processing methods. Additional value‐added operations include milling of whole or dehulled lentils and isolating fractions that are rich in protein and starch that can be used as ingredients in diverse food applications. Lentils are aligned well with the changing consumer trends towards meat alternatives, plant‐based diets, and healthy food options. Furthermore, due to increased environmental concerns for the production of meat, consumers are minimizing or even excluding meat consumption and opting for non‐meat foods produced in a sustainable manner. This review article provides an overview of lentil's production/trade, consumption trends, nutritional profile, value‐added processing, emerging research and development trends, and the role of lentil production in environmental sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".