Production, Characterization, and Industrial Application of Pectinase Enzyme Isolated from Fungal Strains
Why this work is in the frame
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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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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 | 0.000 |
| 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 it