Potential processing technologies for utilization of millets: An updated comprehensive review
Bibliographic record
Abstract
Abstract Millets are tiny grass‐seeded grains that hold major and minor nutrients and chief bioactive components. They are climate flexible and pest‐resistant grains, enhancing the crop system effectively. Millets are now gaining popularity due to their health‐promoting properties for end users. These nonacid‐forming grains are gluten‐free, stabilize blood sugar, lower cholesterol levels, inhibit human colon tumor growth, combat malnourished diseases, control overweight, and have other health‐promoting benefits. However, many food processing technologies are on hand to process millets into a broad array of value‐added products, but still, the implementation in the food processing industries is skimpy at the commercial level. There are many factors right from the farming stage, like unavailability of good quality seeds, suitable machinery, lack of technical knowledge, and the consumer's misconception of millet's sensory properties, all contribute to low demand in the market. However, considering millet's copious potentialities, the research on these grains is grasping the spotlight in the current era. Therefore, millets would greatly increase demand in the market and create boundless avenues to manufacture millet‐based foods on a commercial scale. Hence, the current article intends to comprehensively review millet processing technologies and bioprocessing approaches, including health benefits. In addition, it also highlighted the recent R & D innovations with millets and millet products in the global market, preservation constraints, and future challenges. Practical Applications Millets are the neglected ancient grains of the world, although they are a treasure trove of nutrients and promote alluring health benefits. The current review analysis fosters various notions to bridge a gap between industrialist and consumers for the high‐level production and consumption of millets in various countries. The compiled information comprises deep insights into major food processing technologies for each millet and listed globally available millet‐based products. In addition, it provides the millet shelf‐life issues, which would be helpful for researchers to tackle these issues with millets in the future. The present study advises increasing the high‐value utilization of millet and millet‐based products at commercial scales. This article attracts scientists, industrialists, researchers, scholars, and budding entrepreneurs. Among all the cereals, millets are superior in the nutritional profile, sustainable production patterns, and friendlier to the farmers, planet, and consumers.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".