Reduction in Lentil Cooking Time Using Micronization: Comparison of 2 Micronization Temperatures
Bibliographic record
Abstract
ABSTRACT Laird No. 1 lentils micronized (high‐intensity infrared heat) to give internal temperatures of 138 and 170 °C were compared to unprocessed lentils stored at room temperature. Micronized lentils, which had been tempered to 33% moisture for 16 h, required less force to compress after cooking and contained increased levels of gelatinized starch and pectic substances but decreased levels of soluble protein, phytic acid, and neutral detergent fiber compared to the unprocessed lentils. Cell walls in the micronized lentil were less susceptible to fracture, and the microstructure was more open. Properties of the micronized lentils were better when the internal temperature reached 138 °C. When micronized to an internal temperature of 170 °C, cooked lentils were harder, possibly due to greater moisture losses and a change in the neutral detergent fiber. Micronization resulted in a slight darkening of the lentils, and this was accentuated at 170 °C.
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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".