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Record W2951091586 · doi:10.22215/etd/2018-13248

Effect of Non-Thermal Ultrasound on Inulin from Jerusalem Artichoke and Its Application in Dairy Industry

2018· dissertation· en· W2951091586 on OpenAlexafffund
Hengguang Xu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInulinUltrasoundFood scienceJerusalem artichokeLinear relationshipMicrostructureChemistryMaterials scienceMedicineMathematicsCrystallographyRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.282
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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