Enzyme Encapsulation by Static Mixer Method for Hydrolysis of Lactose
Bibliographic record
Abstract
Enzyme immobilisation has been intensively investigated due to its advantages such as enzyme recovery, reusability and improved stability over a wide range of pH and temperatures.The encapsulation of -galactosidase incarrageenan is presented in this report for potential application in dairy industry.The immobilisation was carried out by emulsifying oil and -carrageenan with a static mixer device.This is a new approach and has the advantage of producing smaller beads (e.g.smaller than 100 m size) which can be used in continuous processing.The main factors tested were the total flow rate through the static mixer (Qt, in the range 220 -440 ml/min) and -carrageenan to oil volumetric fraction ( , in the range 0.05-0.2).The average bead sizes obtained were in the range of 19 -52 m, with smaller sizes obtained with an increase of Qt.The bead sizes decreased with (i) the decrease in emulsified droplets coalescence and oil inclusion in the beads and (ii) with the decrease in the values of WGtop (defined by the weight percentage of beads found underneath the oil layer).The bead performance was tested using lactose and 2-nitrophenyl--galactopyranoside (ONPG) and the kinetic parameters, lactose conversion and stability were determined at the optimum conditions.The attained optimum pH and temperature were 7 (similar to free enzyme) and 21 o C, respectively.The encapsulated -galactosidase tested at optimum conditions in 5% (w/v) lactose solution was able to convert 76.47% of lactose after six days.These findings contribute to the further understanding of the encapsulation technique and demonstrates the potential of usingcarrageenan as an encapsulation material for -galactosidase.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".