Compositional and sensory differences of products of sweet-cream and whey buttermilk produced by microfiltration, diafiltration, and supercritical CO2
Bibliographic record
Abstract
The objectives of this work were to assess the compositional properties and sensory characteristics of ingredients produced by treating sweet-cream and whey-cream buttermilks with microfiltration (MF), diafiltration (DF), and supercritical CO2 (SFE) extraction. Sweet-cream buttermilk (CBM) and buttermilk resulting from churning the residual fat from whey processing (whey buttermilk, WBM) were used. Using MF or microfiltration followed by diafiltration (MF-DF), we obtained resulting retentates that were dried and then were subjected to SFE treatment. Control buttermilks, SFE resulting products, and MF and MF-DF SFE and all treated retentates products totaled 16 samples (2 types×4 treatments×2 batches). Eleven trained panelists assessed samples using descriptive analysis. Sweet-cream buttermilk was higher in protein and lactose, whereas the WBM had similar total protein, mainly β-LG and α-LA but very low lactose. The resulting samples in order of concentration for fat and lactose were control samples>SFE treated>MF treated>DF=MF-SFE and DF-SFE. Sodium dodecyl sulfate-PAGE protein profiling showed negligible casein for WBM versus CBM and less whey proteins for CBM versus WBM, as expected. Whey buttermilk was more yellow, salty, sour, and rancid than CBM. Regarding the treatments, significant differences were obtained on homogeneity, opacity, rancid odor, cardboard and sour flavors, sweet and salty tastes, viscosity, and mouthcoating, where SFE-treated samples showed lowest rancid odor and cardboard flavor.
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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) | 0.000 | 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) | 0.001 | 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".