Development of chickpea beverages through enzymatic treatments: from rapid visco analyser to pilot plant production
Bibliographic record
Abstract
Abstract Plant-based beverage production generally involves enzymatic treatments to overcome technological challenges. Thus, this research aimed to evaluate the potential use of the Rapid Visco Analyser (RVA) to simulate pilot plant (PP) conditions and determine the enzyme(s) and enzyme-concentrations to be used in PP for beverage production using chickpea flour. Thermostable α-amylase (TA), maltogenase (MA), and amyloglucosidase (AMG) were tested individually and in combination at 3 concentrations. Chickpea suspensions with 3% protein, equivalent to milk contribution, were produced at laboratory scale in RVA while simulating PP. Highest viscosity reductions were obtained with the highest enzymatic activity (TA.3 = 1.21 Ceralpha units (CU)/g flour; MA.3 = 0.15 CU/g flour; AMG.3 = 0.69 mg of glucose released/g flour). TA was the most effective in reducing final viscosity and increasing free sugars, particularly combined with TA + MA + AMG. RVA findings were validated in PP-production using TA.3 + MA.3 + AMG.3. Viscosity decreased from 50,697 ± 8,907 cP observed in the control to 4,505 ± 171 cP when using TA.3 + MA.3 + AMG.3. Due to homogenisation, the whiteness index was higher for PP beverages (67–71) than for RVA suspensions (65–67). This study demonstrates the potential of using RVA as a tool to optimise enzyme concentrations for chickpea beverage production and the successful scale-up of the process to PP-level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".