Monitoring the fractionation of a whey protein isolate during dead‐end membrane filtration using fluorescence and chemometric methods
Bibliographic record
Abstract
During membrane-based separation of proteins, changes in protein concentration of the permeate and retentate streams occurs over time. The current work proposes a new approach for monitoring the changes in concentrations of proteins in both permeate and retentate by making use of data collected using fluorescence spectroscopy and intrinsic protein fluorescence analyzed by multivariate statistical techniques. Whey protein isolate consists mainly of alpha-lactalbumin (alpha-LA), beta-lactoglobulin (beta-LG), and small proportion of bovine serum albumin (BSA) and was used as a model system in this study. A fiber optic probe (FOP) was used to acquire multiwavelength fluorescence spectra for permeate and retentate streams at different times during UF-based separation of the components from a multicomponent solution. Multivariate regression models were developed for predicting the concentrations of alpha-LA, beta-LG, and BSA by establishing a calibration model between data acquired using the FOP and the corresponding protein concentration levels measured by size-exclusion chromatography. The model was validated using FOP data that were not previously used for calibration of the regression models. This comparison showed that concentrations of alpha-LA, beta-LG, and BSA could be predicted directly from FOP data within reasonable accuracy by making use of multivariate calibration tools. This approach has several attractive features including that it is nondestructive, fast, and relatively simple to perform. This technique has potential practical applications as it could offer the opportunity for in situ monitoring of membrane filtration processes by tracking individual protein transmission and selectivity of fractionation.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".