Innovative Food Processing Technologies Promoting Efficient Utilization of Nutrients in Staple Food Crops
Bibliographic record
Abstract
With the rapid growth of the global population and the increasing demand for healthier diets, improving the nutrient utilization efficiency of staple food crops has become a critical scientific and industrial challenge, prompting innovation in food processing technologies. This review introduces first the common nutritional challenges in the processing of staple food crops, followed by the comprehensive examination of research aiming to enhance the nutritional quality of staple food crop-based foods through innovative processing technologies, including microwave (MW), pulsed electric field (PEF), ultrasound, modern fermentation technology, and enzyme technology. Additionally, soybean processing is used as an example to underscore the importance of integrating innovative processing technologies for optimizing nutrient utilization in staple food crops. Although these innovative processing technologies have demonstrated a significant potential to improve nutrient utilization efficiency and enhance the overall nutritional profile of staple food crop-based food products, their current limitations must be acknowledged and addressed in future research. Fortunately, advancements in science and technology will facilitate progress in food processing, enabling both the improvement of existing techniques as well as the development of entirely novel methodologies. This work aims to enhance the understanding of food practitioners on the way processing technologies may optimize nutrient utilization, thereby fostering innovation in food processing research and synergistic multi-technological strategies, ultimately providing valuable references to address global food security challenges.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".