Production, Characterization, and Industrial Application of Pectinase Enzyme Isolated from Fungal Strains
Bibliographic record
Abstract
Pectinases are the group of enzymes that catalyze the degradation of pectic substances. It has wide applications in food industries for the production and clarification of wines and juices. The aim of this study was to isolate, screen and characterize pectinase from fungi isolated from various soil samples and evaluate its application in juice clarification. Fungal strains were isolated and screened primarily using 1% citruspectin incorporated potato dextrose agar (PDA) and secondarily using pectinase screening agar medium (PSAM) for pectinolytic organisms. The enzyme was produced by submerged state fermentation and assayed using the dinitro salicylic acid (DNS) method. From 20 different soil samples, 55 fungal isolates were screened primarily and, among them, only 14 isolates were subjected for secondary screening. Out of 14, only four strains showed the highest pectinolytic activity. Among four strains, Aspergillus spp. Gm showed the highest enzyme production at a 48-h incubation period, 1% substrate concentration, and 30 °C temperature. The thermal stability assessment resulted that the activity of pectinase enzyme declines by 50% within 10 min of heating at 60 °C. The optimum temperature, pH, and substrate concentration for the activity of enzyme was 30 °C (75.4 U/mL), 5.8 (72.3 U/mL), and 0.5% (112.0 U/mL), respectively. Furthermore, the yield of the orange juice, the total soluble solid (TSS), and clarity (% transmittance) was increased as the concentration of the pectinase increased, indicating its potential use in juice processing. Overall, the strain Aspergillus spp. Gm was identified as a potent strain for pectinase production in commercial scale.
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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) | 0.000 | 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 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".