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Record W2574420290 · doi:10.5539/jas.v9n2p115

Physicochemical and Sensory Quality of Brown Sugar: Variables of Processing Study

2017· article· en· W2574420290 on OpenAlexvenueno aff
Raphael Della Maggiore Orlandi, Marta Regina Verruma-Bernardi, Simone Daniela Sartório, Maria Teresa Mendes Ribeiro Borges

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSugarAromaFlavorContext (archaeology)Food scienceChemistryTasteCommercializationQuality (philosophy)SweetnessBusinessMarketingBiology

Abstract

fetched live from OpenAlex

The lack of standardization in the processing of brown sugar reflects in its physicochemical and sensory quality and, consequently, harms the small producers and cottage industries in the products commercialization. In this context, this work aimed to study the influence of the variables – period of the year, variety of sugar cane, pH and final temperature of juice cooking – on the acquisition of a product that is acceptable according to the physicochemical and sensory requisites. The physicochemical parameters of the sugars that best classified the product presented juice neutral pH (7.0) and finalization temperature at 118 oC for both varieties, in the late period. In the sensory aspect, the sugars of the variety RB92579 found, in a general context, greater acceptance, being classified, also by the judges, as sugars of a darker appearance, smaller granules, less intense sweet aroma and flavor, and high solubility.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.359
Teacher spread0.275 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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