Processing effects on four prebiotic carbohydrates supplemented in an extruded cereal and a low pH drink
Bibliographic record
Abstract
Prebiotic carbohydrates are added as functional ingredients to a variety of processed foods. Data on the stability of prebiotics during food processing in complex matrices remain limited. The objective of this project was to determine the stability of fructooligosaccharides (FOS), inulin, galactooligosaccharides (GOS), and resistant starch (RS2), when added as ingredients (1% w/w) to an extruded cereal and a low pH drink. The cereal was prepared using different screw speeds and barrel temperatures. GOS was not affected by any of the extrusion conditions, whereas inulin decreased significantly at 140 and 170°C. FOS levels decreased in all extrusion conditions, while resistant starch (RS) unexpectedly increased for each of the parameters. The low pH drink was prepared with different sucrose to corn syrup solids (S:CSS) ratios (1:2, 1:1, 2:1) at pH 3.0, 3.5, and 4.0. The 1:1 S:CSS drink at pH 3.0, negatively impacted FOS and inulin. Moreover, FOS levels decreased when exposed to 1:2 S:CSS (pH 3.5 and 4.0) and 1:1 S:CSS (pH 3.0). GOS and RS were unaffected by any drink formulations. As different conditions impact the stability of prebiotics differently, this study addresses the importance of developing product specific processes for each prebiotic when supplemented into a processed food.
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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 | 0.001 |
| 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".