Effect of lentil flour incorporation on the sensory, nutritional, and functional properties of noodles
Bibliographic record
Abstract
Abstract This study focuses on the development and evaluation of lentil-infused noodles as a nutritious and functional food alternative for health-conscious consumers. Various formulations of lentil flour (10%, 20%, 30%, and 40%) were combined with wheat flour to produce lentil-enriched noodles. The sensory attributes, nutritional composition, functional properties, and storage stability of these noodles were assessed. Sensory evaluation revealed that the formulation with 30% lentil flour (LIN-30) had the highest acceptability, particularly in terms of flavor and texture. Proximate composition analysis showed that LIN-30 had significantly higher protein (15.84 g/100 g) and fiber (3.14 g/100 g) content compared to control wheat noodles. Additionally, LIN-30 exhibited a moderate glycemic index of 60.57 ± 0.07, making it suitable for individuals looking to manage blood sugar levels. In vitro protein digestibility was also favorable, with a digestibility rate of 72.59 ± 1.12%. The mineral content of LIN-30 was enriched, showing higher levels of iron, zinc, and manganese than the control noodles. Texture profile analysis revealed that LIN-30 had good firmness and elasticity, although slightly lower than that of control noodles. Furthermore, LIN-30 demonstrated good storage stability, maintaining its nutritional and sensory properties over a three-month period. Overall, lentil-infused noodles, especially the LIN-30 formulation, offer a promising, affordable, and nutritious option for individuals seeking healthier noodle alternatives.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".