Effect of infrared heating on the nutritional properties of yellow pea and green lentil flours
Bibliographic record
Abstract
Abstract Background and Objectives To enhance the utilization of pulse ingredients, greater knowledge of the effect of infrared (IR) processing on protein and starch nutrition is needed. The current study investigated the use of tempering (20% vs. 30% moisture) with IR heating (120°C vs. 140°C) to improve the nutritional value of two commercially important pulses: green lentils and yellow peas. Findings Proximate composition remained mostly unchanged after IR heating for both pulse types. The protein's secondary structure transitioned to a state with a higher amount of random coils as IR processing conditions intensified (increase in moisture and temperature). In vitro protein digestibility (IVPD) increased from 73% to 78%−82% for green lentil and 78% to 81%−85% for yellow pea, depending on IR processing treatment. Tryptophan was the limiting amino acid in all samples. The IVPD corrected amino acid scores were not significantly altered by IR processing. The content of rapidly (RDS) and slowly (SDS) digestible starches increased, whereas that of resistance starch declined with IR processing. Conclusions The combined effect of tempering moisture and IR heat as a premilling treatment changed the protein secondary structure but did not improve the overall protein quality of the pulses. Starch digestibility was improved with IR processing. Significance and Novelty Employing tempering and IR heating techniques on pulses may be useful in food and feed applications where improved starch digestibility is desired.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".