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Impact of Production Practices on Organoleptic Intensity Scale of Different Rice Cultivars

2015· article· en· W653804756 on OpenAlexvenueno aff
Amit Kesarwani, Madhu Sharma, Sachin Kumar Vaid, Shih Shiung Chen

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsnot available
Fundersnot available
KeywordsOrganolepticAromaCultivarFlavorSensory analysisFood scienceMathematicsAmyloseAgricultureAgronomyBiotechnologyBiologyStarchEcology

Abstract

fetched live from OpenAlex

The purpose of this research was to relate mean organoleptic scores of organically and conventionally grown rice (n = 5) in japonica cultivars (Taikeng No. 16 and Kaohsiung No. 139). The 0-7 organoleptic scale is used in trials to measure the agronomic practices impact on sensory attributes of rice cultivars. However, the precise relationship between farming system and organoleptic analysis of rice remains independent variables. Judges (n = 10) used a common 0-7 scale to report the 6 sensory attributes viz. appearance, aroma, flavor, cohesion, hardness and overall acceptability while keeping cultivar Taikeng No. 9 as control. The scale ranges from – 3 to + 3 as very poor to excellent. The study demonstrates sensory attributes as inherited trait of rice; while no improvement found in cooking and eating quality under seasonal or agronomic variations. Interestingly, the aroma was reported as only better parameter when grown under organic farming compared to conventional farming (– 0.49 and – 0.62 over control, respectively). Also, the positive co-relationship exists between amylose content and organoleptic analysis while antagonistic link to crude protein content. The study cleared that management method, per se, did not influence any flavory attributes and detected no changes by the sensory panel. Further descriptive analysis needed with different conditions such as variety, degree of milling, growing location and moisture content which also played significant role in determining flavor and eating quality of rice cultivars.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.085

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 integrity0.0000.000
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 head0.155
GPT teacher head0.414
Teacher spread0.259 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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