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Record W2898846350 · doi:10.1515/ijfe-2018-0184

Comparison of Germination-Parboiling, Freeze-Thaw Cycle and High Pressure Processing on Phytochemical Content and Antioxidant Activity in Brown Rice Evaluated after Cooking and In-Vitro Digestion

2018· article· en· W2898846350 on OpenAlexaff
Yong Yu, Yang Du, Hosahalli S. Ramaswamy, Hao Wang, Xiuping Jiang, Songming Zhu

Bibliographic record

VenueInternational Journal of Food Engineering · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsMcGill University
FundersUniversité de StrasbourgNational Natural Science Foundation of China
KeywordsPhytochemicalParboilingChemistryFood scienceAntioxidantABTSDigestion (alchemy)GerminationBrown riceOxygen radical absorbance capacityIn vitroBotanyChromatographyBiochemistryAntioxidant capacityBiologyDPPH

Abstract

fetched live from OpenAlex

Abstract Three treatments, namely germination-parboiling (PG), freeze-thaw cycle (FTC) and high pressure processing (HPP) were compared for phytochemical content and antioxidant activity of brown rice (BR). These were determined in raw (uncooked), cooked, and in-vitro digested BR and compared with those from untreated BR and white rice (WR). PG showed the highest retention of phytochemicals after cooking (87–100%) while it dropped to 59–72% with FTC and 64–76% with HPP. After in-vitro digestion, the highest amount of phenolics was found in PG-24 h and flavonoids in FTC for two cycles. The antioxidant activity, as determined by oxygen radical absorbance capacity and ABTS methods, showed the highest value to be associated with in-vitro digested sample of PG-24 h, and lowest in WR. The results of this study show that these three treatments could improve or retain the phenolic content and antioxidant activity in cooked BR after in-vitro digestion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.028
GPT teacher head0.293
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2018
Admission routes1
Has abstractyes

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