Production Process Effect on Mexican Agave Syrups Quality: A Preliminary Study
Bibliographic record
Abstract
In this work, the quality parameters of commercial agave syrups produced in five different Mexican states and with different production process were evaluated. Regulated parameters (pH, Moisture, Ashes, and 5-(hydroxymethyl) furfural), as well as color and aw, were measured on 25 agave syrups, including traditional samples as controls. Traditional and semi-industrial syrups were samples obtained by thermal hydrolysis. Additionally, the semi-industrial process included control of process variables such as pH, °Bx, and temperature. The industrial process is technified. The agave syrups ranged from 70-76 °Bx, pH ranged from 3.2-6.7, and moisture from 20.2-28.6%. The aw values shown a wide variation as well as L* a* and b* color parameters. Some of those parameters shown significant differences in ANOVA analysis; however, most of the samples complied with the norm. General Discriminant Analysis (GDA) made it possible to discriminate between production process by using pH, % Ash, b*, 5-(hydroxymethyl)furfural and a* parameters. Further analysis of a wide range of syrups and the inclusion of non-regulated compounds such as volatile compounds and carbohydrates are needed to get more information for a deeper characterization of agave syrups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".