In vitro protein digestibility and physico‐chemical properties of lactic acid bacteria fermented beverages enriched with plant proteins
Bibliographic record
Abstract
The objective of this study was to develop probiotic beverages, enriched with plant proteins, with high nutritional value. A rice-based beverage fermented with a specific probiotic formulation comprised Lactobacillus acidophilus CL1285, Lactobacillus casei LBC80R and Lactobacillus rhamnosus CLR2 has been enriched with a combination of pea and rice proteins (PR) or pea and hemp proteins (PH) at 13 and 11% total protein, respectively. These protein associations have been selected because their amino acid ratio was >1, as recommended by the FAO. The beverage enriched with protein significantly increased its viscosity by more than 10 times thanks to the enrichment, while the fermentation reduced it by 50% for PR and 20% for PH. In vitro protein digestibility results showed that the protein enrichment and the fermentation treatment significantly increased digestibility values of the beverages with value of 72.7% for fermented PR beverage and 61.4% for unenriched fermented control beverage (p ≤ 0.05). Peptide profiles of PR and PH enriched beverages indicated that the fermentation led to a reduced level of high molecular weight (HMW) peptides of about 60% and an increase of low molecular weight (LMW) peptides by over 50%. Therefore, both the fermentation and the enrichment in protein increased the nutritional value of the rice-based beverages. PRACTICAL APPLICATION: Good quality of probiotics formulation and high-protein products are in increasing demand and plant proteins as an alternative of animal protein are popular. This study has permit to develop rice-based commercial probiotic beverages enriched in a combination of pea and rice or pea and hemp proteins in order to obtain a complete protein in terms of amino acids composition. The lactic acid fermentation and the enrichment with a plant protein combination led to a better protein digestibility of beverage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".