Effect of Non-Thermal Ultrasound on Inulin from Jerusalem Artichoke and Its Application in Dairy Industry
Bibliographic record
Abstract
Jerusalem artichoke (JA) is a rich source of dietary fiber.The major dietary fiber inside of JA is inulin which is a heterogeneous collections of fructose polymers with many health benefits for humans.To investigate the effect of ultrasound on the inulin from JA, JA powder, Purified JA inulin (PJAI) was treated with 20KHz ultrasound compared with chicory inulin (CI).Ultrasound treatment time had a positive linear relationship with reducing sugar content of these samples.After ultrasound treatment, reducing sugar content in JA powder increased from 6.101g/100g up to 12.273g/100g.In PJAI, reducing sugar content increased from 10.378g/100g up to 12.274g/100g.Reducing sugar content in CI increased from 1.126g/100g up to 2.183g/100g.Also, determined by HPLC and GPC, for PJAI, there was a negative linear relationship between high degree of polymerization (DP) inulin with ultrasound treatment time (R 2 =0.9169), and a positive linear relationship between low DP inulin with ultrasound treatment time (R 2 =0.9738), which was not observed for inulin from chicory.Furthermore, JA powder was added into whey and milk and treated with 20KHz ultrasound to investigate the structure change.Ultrasound changed the microstructure of milk resulting in a flatter surface for milk and whey.Ultrasound combined particles of milk and whey This kind of phenomenon was much more obvious only for milk when mixed with JA powder.
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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".