Bibliographic record
Abstract
Pea is a nutritious legume that can be used as an animal protein substitute. However, the unpleasant greeny aroma severely inhibits the application of pea-derived proteins as a food ingredient. This study aims at improving the aroma of pea protein isolates (PPIs) with an aim to employ these PPIs for plant-based dairy substitute production. Major objective of this study is to establish the applicability of lactic acid fermentation (LAF) in eliminating unpleasant aroma from PPIs while maintaining the functional properties and protein quality. We hypothesize that LAF treatment can reduce the undesirable aroma of pea protein isolates by virtue of desirable bacterial action on plant proteins. A solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME-GC-MS) method was developed in this study to identify and quantify the volatile compound profile of plant-based protein. Total concentration of volatile compounds belonging to aldehyde, ketone and alcohol group in the pea, soy and brown rice protein is analyzed and compared. Different LAF treatments are performed with Lactobacillus plantarum, Lactobacillus casei and mixed strains of probiotics. The protein quality of treated and untreated protein sample is analyzed by the Bradford protein assay and sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). Evolution of functional properties including emulsifying properties, foaming properties, water holding and oil binding capacities of samples with different times of lactic acid fermentation treatments are also analyzed. Based on the experimental results, the water-soluble protein content decreased with the increase of fermentation time, with protein configuration majorly altered beyond 15-hour of fermentation. Thus, a 10-hour L. plantarum fermentation was found optimal in eliminating off-flavour while maintaining protein quality and functional properties. For the optimal treatment, around 42% aldehyde and 64% ketone content were removed, and a small amount of alcohol was produced. This change of the aroma compound profile is considered desirable for dairy substitute production. Results from a descriptive analysis sensory test showed that LAF treatment successfully decreased the overall aroma and flavour intensity in PPIs. The results of this study could be used by the Canadian Food Industry for pre-treating pea protein isolates, before using them as an ingredient in plant-based dairy substitutes.
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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 | 0.000 | 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 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".