Generating Multi-Functional Pulse Ingredients for Processed Meat Products—Scientific Evaluation of Infrared-Treated Lentils
Bibliographic record
Abstract
In the last decade, various foods have been reformulated with plant protein ingredients to enhance plant-based food intake in our diet. Pulses are in the forefront as protein-rich sources to aid in providing sufficient daily protein intake and may be used as binders to reduce meat protein in product formulations. Pulses are seen as clean-label ingredients that bring benefits to meat products beyond protein content. Pulse flours may need pre-treatments because their endogenous bioactive components may not always be beneficial to meat products. Infrared (IR) treatment is a highly energy-efficient and environmentally friendly method of heating foods, creating diversity in plant-based ingredient functionality. This review discusses using IR-heating technology to modify the properties of pulses and their usefulness in comminuted meat products, with a major emphasis on lentils. IR heating enhances liquid-binding and emulsifying properties, inactivates oxidative enzymes, reduces antinutritional factors, and protects antioxidative properties of pulses. Meat products benefit from IR-treated pulse ingredients, showing improvements in product yields, oxidative stability, and nutrient availability while maintaining desired texture. IR-treated lentil-based ingredients, in particular, also enhance the raw color stability of beef burgers. Therefore, developing pulse-enriched meat products will be a viable approach toward the sustainable production of meat products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".