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Record W2576561369 · doi:10.3390/foods6010009

Health-Promoting Compounds in Pigmented Thai and Wild Rice

2017· article· en· W2576561369 on OpenAlexaboutno aff
Valentina Melini, Rita Acquistucci

Bibliographic record

VenueFoods · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsnot available
FundersMinistero delle Politiche Agricole Alimentari e Forestali
KeywordsBiologyHealth benefitsTraditional medicineFood scienceBiotechnologyChemistryMedicine

Abstract

fetched live from OpenAlex

Organic pigmented Thai rice and wild rice are commonly available in specialized Italian markets and food shops, and they are gaining popularity among consumers demanding healthy foods. Indeed, the typical colour of kernels, which is the unique characteristic of pigmented cereals, is due to the accumulation of pigments that are also responsible for a number of healthy effects. The aim of this study was to provide a portrait of two organic pigmented Thai rice varieties from Thailand and one wild rice variety from Canada, imported into Italy and at Italian consumers' disposal. To this end, the proximate composition and the content of health-promoting compounds, such as carotenoids, anthocyanins and phenolic compounds, were determined in Thai and wild rice. Moreover, the effect of cooking on phytochemicals was assessed, in order to provide reliable data on the dietary intake of bioactive compounds by samples under investigation. Results show that studied samples have a content of phytochemicals higher than white rice and comparable to other cereals. The cooking process determined a decrease of bioactive compounds in all varieties under investigation. However, some samples were found more resistant to cooking stress, and some phytochemicals were little affected by this process. Therefore, pigmented Thai and wild rice may represent a valuable source of healthy compounds and an alternative to other wholesome foods required by consumers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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 designObservational
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

Citations43
Published2017
Admission routes1
Has abstractyes

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