Διαχωρισμός και ανάκτηση πρωτεϊνών και σακχάρων από τυρόγαλο χαλλουμιού με τη χρήση μεμβρανών υπερδιήθησης
Bibliographic record
Abstract
The current thesis investigated the separation and recovery of proteins and sugars from halloumi whey. During the process of halloumi manufacturing, big volumes of whey is produced as waste. The whey is high in organic matter and its processing is a costly process. The objective of the current study was to investigate the separation and the recovery of high added value components (proteins and sugars) as byproducts of halloumi whey. For this purpose, two samples of industrially produced whey were processed using five types of ultrafiltration membranes with different Molecular Weight Cut Offs (MWCO: 100, 50, 20, 2, 1 kDa) under various transmembrane pressures. The experiments were performed in a laboratory size membrane apparatus (Alfa Laval Labunit M10). In the first part of the experiment, ultrafiltration was conducted using three types of membranes cutoff 100, 50 and 20 kDa. Two replicates were performed for each sample and each type of membrane. Retention coefficients and performance parameters were monitored for each experiment. Feed and permeate was collected for analysis. In the second part of the experimental procedure permeate from the 20 kDa membrane was used as feed for the membranes treated with 2 and 1 kDa, and similar procedures as the first part were followed. Performance parameters and coefficients were studied for each experimental combination. Analyses were made for retention of protein, total sugars and total phenols. According to the results, all the membranes had a high retention rate of protein (70-90%). The disadvantages of membranes with smaller MWCO were the relatively high retention of total sugars in the concentrate (20-40%) and the low flux. However, the optimum separation was performed using the 100 kDa membrane which led to ~ 75% of proteins remaining in the concentrate, while ~ 98% of total sugars passed in the filtrate achieving the separation of two components. The optimum operating parameters were at a pressure of 3 bar where the flux was ~ 12 Lm -2h-1 and the relative flux was around 50%.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.042 |
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".