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Record W2976602845 · doi:10.5539/jas.v11n17p210

Microbiological and Microscopic Analysis of Sugarcane Syrup

2019· article· en· W2976602845 on OpenAlexvenueno aff
Jhéssica Samara Abreu Holsbach Belé, Carolina Medeiros Vicentini‐Polette, Sandra Regina Ceccato‐Antonini, Marta Helena Fillet Spoto, Valdinei Luís Belini, Marta Regina Verruma-Bernardi

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSugarcane Cultivation and Processing
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSaccharum officinarumFood scienceSugarMesophileBagasseCaneChemistryBiotechnologyBacteriaBiologyBotany

Abstract

fetched live from OpenAlex

Sugarcane syrup is defined as the product obtained by the concentration of sugarcane juice (Saccharum officinarum L.) or from melted cane rapadure. This product has good acceptance in the Brazilian market and can be used as a sweetener in substitution of refined sugar, besides containing important minerals. This study analyzed samples of sugarcane syrup based on its microbiological and microscopic properties. In total, 15 commercial brands of sugarcane syrups were analyzed. No brands had the presence of flat-sour thermophilic bacteria, total coliforms or Escherichia coli, while five brands were contaminated with mesophilic bacteria, molds, and yeasts. Microscopic analysis, performed under optical light transmission microscopy, revealed that 14 (93%) brands contained some kind of dirt or foreign material, with only one brand (M) according to the standards. The Brazilian standard in force (RDC, 2001) specifies the microbiological standards for food but does not contain important information for sugarcane syrup, and an update is required.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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