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Record W3135393632 · doi:10.1002/star.202000196

Fortifying compounds reduce starch hydrolysis of potato chips during gastro‐small intestinal digestion in vitro

2021· article· en· W3135393632 on OpenAlexaff
Yudy Duarte‐Correa, Oscar Vega‐Castro, Nataly López‐Barón, Jaspreet Singh

Bibliographic record

VenueStarch - Stärke · 2021
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Alberta
FundersDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsStarchFood scienceFortificationDigestion (alchemy)ChemistryHydrolysisGlycemic indexPotato starchSwellingVitaminGlycemicBiochemistryChromatographyBiotechnologyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Abstract Potato chips are the most widely consumed snack in the world; therefore, they can be used as an ideal carrier for nutrient delivery. The objective of this study is to investigate the effect of impregnating vitamin E, vitamin C, and calcium through vacuum impregnation (VI) on the physico‐chemical, microstructural, and starch hydrolysis (%) in vitro and estimated glycemic index ( eGI ) of potato chips from a variety grown in Colombia. The fortification process decreased the peak viscosity from 1428 ± 20 cP (unfortified) to 734 ± 27 cP (fortified) and increased pasting temperatures. The swelling power also decreased from 11.36 ± 0.32 g/g (unfortified) to 8.59±0.07 g/g (fortified) after the fortification. Approximately 42% and 63% of the starch is hydrolyzed in the first 10 min of small intestinal digestion for the fortified and control samples, respectively. At the end of in vitro digestion (120 min), fortified potato has 74% starch hydrolyzed, whereas the same is calculated at 95% for control samples. The microstructural characteristics of digesta obtained during in vitro digestion showed that fortified samples have well preserved cell structures. In conclusion, the addition of impregnation compounds led to a lower starch hydrolysis of potato chips with a consequent decrease in eGI .

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 categoriesnone
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 score0.044
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.278
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2021
Admission routes1
Has abstractyes

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