INTERPRETATION OF THE FORCE–DEFORMATION CURVES OF COOKED RED LENTILS (<i>LENS CULINARIS</i>)
Bibliographic record
Abstract
ABSTRACT The effect of cooking time on the textural properties of red lentils was determined using an Instron universal testing machine equipped with an Ottawa texture cell. A sigmoid‐shaped force–deformation curve was observed for all samples. As cooking time increased, texture changes, in terms of undercooked and optimally cooked, were identified by changes in slope and plateau force values of the force–deformation curves. At short cooking times, the samples were undercooked, and slope and plateau force values were high. At prolonged cooking times, slope and plateau force values decreased to a certain point and became independent of cooking time as values leveled off. However, significant textural changes as determined with sensory methods continued. Cooking time affected the location of the inflection point on the force–deformation curve. Deformation at inflection was a parameter that successfully determined textural differences between cooked samples and overcooked samples. Force–deformation curves can describe cooking quality of red lentils. PRACTICAL APPLICATIONS There is little information available on the instrumental methods used to measure the texture of red lentils despite the fact that red lentils account for the majority of world lentil production and trade. Thus, there is a need to demonstrate how the force–deformation curves resulting from the instrumental measurement of red lentil texture are affected by cooking. A detailed interpretation of a force–deformation curve would allow for investigation of the effects of biochemical differences in red lentils because of genotype, agronomic practices and postharvest handling on the cooking quality. This work used an Instron universal testing machine equipped with an Ottawa texture cell to obtain force–deformation curves that were interpreted and used to explain the effects of cooking on the textural properties of red lentils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".