Effect of Processing on Postprandial Glycemic Response and Consumer Acceptability of Lentil-Containing Food Items
Bibliographic record
Abstract
The consumption of pulses is associated with many health benefits. This study assessed post-prandial blood glucose response (PPBG) and the acceptability of food items containing green lentils. In human trials we: (i) defined processing methods (boiling, pureeing, freezing, roasting, spray-drying) that preserve the PPBG-lowering feature of lentils; (ii) used an appropriate processing method to prepare lentil food items, and compared the PPBG and relative glycemic responses (RGR) of lentil and control foods; and (iii) conducted consumer acceptability of the lentil foods. Eight food items were formulated from either whole lentil puree (test) or instant potato (control). In separate PPBG studies, participants consumed fixed amounts of available carbohydrates from test foods, control foods, or a white bread standard. Finger prick blood samples were obtained at 0, 15, 30, 45, 60, 90, and 120 min after the first bite, analyzed for glucose, and used to calculate incremental area under the blood glucose response curve and RGR; glycemic index (GI) was measured only for processed lentils. Mean GI (± standard error of the mean) of processed lentils ranged from 25 ± 3 (boiled) to 66 ± 6 (spray-dried); the GI of spray-dried lentils was significantly (p < 0.05) higher than boiled, pureed, or roasted lentil. Overall, lentil-based food items all elicited significantly lower RGR compared to potato-based items (40 ± 3 vs. 73 ± 3%; p < 0.001). Apricot chicken, chicken pot pie, and lemony parsley soup had the highest overall acceptability corresponding to “like slightly” to “like moderately”. Processing influenced the PPBG of lentils, but food items formulated from lentil puree significantly attenuated PPBG. Formulation was associated with significant differences in sensory attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".