Sensory descriptors for pulses and pulse‐derived ingredients: Toward a standardized lexicon and sensory wheel
Bibliographic record
Abstract
The organoleptic quality of pulses and their derived ingredients is fundamental in human utilization and evolution of food. However, the widespread use of pulses is hindered by their inherent sensorial aspects, which are regarded as atypical by the consumers who are unfamiliar to them. In most studies involving sensory assessment of pulses and pulse-ingredients using classical descriptive analysis methods, assessors establish their own lexica. This review is a synthesis of descriptive terms by which sensations emanating from pea, chickpea, lentil, faba bean, dry bean, bambara groundnut, lupin, pigeon pea and cowpea, and their derived ingredients have been described in the literature. Studies involving sensory assessment of processed whole seeds, slurries of raw flour, slurries of protein extracted from raw flour, and food products containing components of pulses were considered. The terms are categorized into those denoting basic taste, aroma, flavor, and trigeminal sensations. Bitterness is the most widely perceived basic taste. Beany, which is broad and complex with subcharacter notes, is predominantly used to describe aroma and flavor. The frequency of use of the collated terms in the reviewed studies was used to establish a sensory wheel. Inconsistency in the use of descriptive terms in the literature necessitates establishment of a standard lexicon that can be applied in both classical and increasingly popular rapid descriptive methods (e.g., check-all-that-apply) throughout the pulse value chain. This review is timely considering the dominance of pulses in plant-based foods and their increasing appeal to the food industry.
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".