MétaCan
Menu
Back to cohort
Record W2158437764 · doi:10.5539/jfr.v3n2p82

Physicochemical, Cooking Characteristics and Textural Properties of TOX 3145 Milled Rice

2014· article· en· W2158437764 on OpenAlexaffvenue
Amaka Odenigbo, Michael Ngadi, Chijioke Kingsley Ejebe, Noé Woïn, Sali Atanga Ndindeng

Bibliographic record

VenueJournal of Food Research · 2014
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsLightnessFood scienceAmyloseChemistryMathematicsStarchPhysics

Abstract

fetched live from OpenAlex

Quality of rice is an important criterion for the choice and demand by rice consumers and it is determined by physicochemical parameters. The objective of this research was to screen the physical, gelatinization, cooking and textural properties of an improved rice variety cultivated in Cameroon (TOX 3145). Three differently processed samples of TOX 3145: non-parboiled (NP), traditional parboiled (TP) and IRAD parboiled (IRAD) were involved in this study. The result revealed the grain dimension of samples as long and slender shape. The degree of redness among cooked and uncooked grains varied from -0.8 to -1.0 and 0.3 to 1.5, respectively while yellowness parameter ranged between 0.4 to 4.0 and 7.6 to 8.4, respectively. Lightness parameter (L*) varied from 59.4 to 61.8 in cooked samples. Minimum cooking time among samples was between 17.9-19.7 min. Highest elastic modulus and hardness (43.3 N/mm and 36.8 N, respectively) was found in TP sample. The NP sample had lowest adhesiveness (-0.76 J) and highest gumminess (6.40 J). Water uptake was positively correlated with amylose content (r = 0.84; P < 0.05) and lightness parameter (r = 0.92; P < 0.05).

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)0.001
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 score0.014
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.076
GPT teacher head0.324
Teacher spread0.248 · 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

Citations25
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicFood composition and propertiesFrench-language works237,207