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Sensory assessment of pressure-cooked and pureed pulses reveals similarities for chickpea/yellow pea and dry bean/faba bean

2024· preprint· en· W4402739659 on OpenAlexafffund
Claire M. Chigwedere, Janitha P.D. Wanasundara, P.J. Shand

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food Canada
KeywordsDry beanMung beanHorticultureBiologyAgronomyPhaseolus

Abstract

fetched live from OpenAlex

The dominance of pulses in plant-based protein foods necessitates an investigation into their organoleptic properties. Taste, aroma, flavor, and trigeminal attribute intensities of purees obtained from pressure-cooked black bean, chickpea, faba bean, green lentil, pinto bean, and yellow pea were assessed using a trained panel. All pulse purees had similar dry matter content and relatively similar particle size distribution profiles; thus, these factors would not influence the intensities of the sensory attributes. The pulse-like attribute was consistently rated highly across all the pulse purees. The black, pinto, and faba bean purees mostly exhibited similar characteristics whilst chickpea and yellow pea purees behaved similarly. For example, the dry beans and faba bean purees had higher intensities for the bitter, earthy, and metallic aromas and lower intensities for the green, floury/starchy, sweet, and nutty aromas than those for the chickpea and yellow pea purees. Interestingly, green lentil puree largely exhibited intensities that were typical of dry bean purees. Some attributes were perceived to be more intense when assessed as tastes than aromas or vice versa. The same was also observed for attributes that were assessed as both aroma and flavor. It was found that the sensitivity of the mode of perception on the perceived intensity of an attribute may depend on the type of pulse. The similarities in sensory profiles of the pulses can be useful to first, the food industry to expand on ingredients in formulations without drastic effects on the sensory quality and second, the consumers who are neophobic.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.258
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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