Evaluation of Macerating Pectinase Enzyme Activity under Various Temperature, pH and Ethanol Regimes
Bibliographic record
Abstract
The polygalacturonase (PGU), hemicellulase (mannanase) and protease enzyme activities in commercial macerating, pectinase-enzyme preparations commonly used by wineries in Ontario (Scottzyme Color X and Color Pro) were measured under various simulated process conditions (temperature, pH, and ethanol concentration). Treatments included three temperatures (15, 20 and 30 °C; pH = 3.0, 3.5, 4.0 and 5.0; ethanol = 0%), four pH levels (3.0, 3.5, 4.0 and 5.0; temperature = 15, 20, 30 and 50 °C; ethanol = 0%), and four ethanol concentrations ((2.5, 5, 7.5 and 10%); temperature = 20 °C and pH = 3.5.) Polygalacturonase enzyme activity in Color X increased linearly with temperature at all pH levels, and increased with pH at all temperature regimes. Polygalacturonase activity decreased with increasing ethanol. Color X mannanase activity increased with temperatures between 15 and 40 °C, and decreased with increased pH between 3.0 and 5.0. Response of mannanase to ethanol was cubic with a sharp decrease between 8 and 10% ethanol. Protease activity increased linearly with temperatures between 20 and 40 °C. These data suggest that the PGU, mannanase and protease components in these enzyme products provide sufficient activities within the ranges of pH, temperature, and ethanol common during the initial stages of red wine fermentations, although low must temperatures (<20 °C) and presence of ethanol would likely lead to sub-optimal enzyme activities.
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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) | 0.001 | 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".