Sensory and Physicochemical Studies of Thermally Micronized Chickpea ( <i>Cicer arietinum</i> ) and Green Lentil ( <i>Lens culinaris</i> ) Flours as Binders in Low‐Fat Beef Burgers
Bibliographic record
Abstract
Pulses are known to be nutritious foods but are susceptible to oxidation due to the reaction of lipoxygenase (LOX) with linolenic and linoleic acids which can lead to off flavors caused by the formation of volatile organic compounds (VOCs). Infrared micronization at 130 and 150 °C was investigated as a heat treatment to determine its effect on LOX activity and VOCs of chickpea and green lentil flour. The pulse flours were added to low-fat beef burgers at 6% and measured for consumer acceptability and physicochemical properties. Micronization at 130 °C significantly decreased LOX activity for both flours. The lentil flour micronized at 150 °C showed a further significant decrease in LOX activity similar to that of the chickpea flour at 150 °C. The lowering of VOCs was accomplished more successfully with micronization at 130 °C for chickpea flour while micronization at 150 °C for the green lentil flour was more effective. Micronization minimally affected the characteristic fatty acid content in each flour but significantly increased omega-3 and n-6 fatty acids at 150 °C in burgers with lentil and chickpea flours, respectively. Burgers with green lentil flour micronized at 130 and 150 °C, and chickpea flour micronized at 150 °C were positively associated with acceptability. Micronization did not affect the shear force and cooking losses of the burgers made with both flours. Formulation of low-fat beef burgers containing 6% micronized gluten-free binder made from lentil and chickpea flour is possible based on favorable results for physicochemical properties and consumer acceptability.
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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 | 0.001 |
| 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".