<i>In Vitro</i> Bile Acid Binding and Short-Chain Fatty Acid Profile of Flax Fiber and Ethanol Co-Products
Bibliographic record
Abstract
Fibers from flaxseed and co-products from ethanol production could be potential sources of dietary fiber in human diet. In vitro fermentation and bile acid binding models were used to investigate the metabolic effects of lignaMax (Bioriginal Food and Science Corp., Saskatoon, SK, Canada) flax meal, spent flax meal, soluble flax gum, wheat insoluble fiber (WIF), and rye insoluble fiber (RIF). Wheat and rye bran were used as reference samples. Bile acid binding of substrates was analysed at taurocholate ([(14)C]taurocholate) concentration of 12.5 mM. Soluble flax gum showed the highest bile acid binding (0.57 micromol/mg of fiber) (P <or= .05). There was no difference in bile acid binding between wheat bran (0.2 micromol/mg of fiber) and WIF (0.26 micromol/mg of fiber). RIF had higher (P <or= .05) bile acid binding (0.20 micromol/mg of fiber) than rye bran (0.13 micromol/mg of fiber). Substrates were hydrolyzed and incubated with pig fecal samples. Short-chain fatty acid (SCFA) profile and gas accumulation (G(f)) were compared. Soluble flax gum generated the highest amount of acetic and propionic acids. SCFA profiles of wheat/rye brans and WIF/RIF were similar (except for butyric acid). G(f) for soluble flax gum was greater (P < .001) than that of spent flax meal. G(f) values of the wheat samples were similar, whereas the G(f) of the rye bran was higher (P < .001) than that of RIF. Fractional degradation rate (micro(t = T/2)) (P < .001) was also recorded. The highest mu(t = T/2) was observed for the soluble flax gum. Oil-depleted flaxseed fractions and WIF/RIF (co-products from ethanol production) could be potential sources of dietary fiber in human nutrition.
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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".