The value of tamarind (Tamarindus indica L.) pulp and its potential use in vinegar production
Bibliographic record
Abstract
Background: The objectives of this work are to study the physicochemical properties of tamarind fruit pulp for production of vinegar and to evaluate the quality characteristics of the produced vinegar.Methods: Two samples of tamarind were used in this study, sample A from Eastern Sudan and B from Western Sudan. They were analyzed for their physical, chemical composition and minerals content.Results: The acidity (%), total soluble solid, TSS Brix and pH values of tamarinds fruit pulp showed that sample A scored the higher value 28.87%, 42.2 Brix in acidity and TSS, respectively. All proximate analysis parameters determined were significantly different (P≤ 0.05) in the two samples except the ash content. Results showed that tamarind samples contain high amounts of Ca (149.13 – 174.19 mg/100g), in addition to Fe (68.94 – 80.67 mg/100g). The P content of the two samples was very low (3.63 – 9.06 mg/100g). For vinegar production, the fruit pulp was first digested and fermented with Saccharomyces cerevisae, and then acidified with Acetobacteraceti. No significant difference (P ≥0.05) was observed between samples A and B in refractive index, density (g/cm3) and pH between the two samples. After acidification step, the same properties were also estimated, the result were 1.35, 0.95(g/cm3) and 2.05, respectively for sample A, and 1.34, 0.94(g/cm3), 2.18 for, respectively for sample B. Sample B had the higher concentration vinegar (18.14%) compared to sample A (17.18%).Conclusion: tamarind fruit pulp could be recommended for vinegar production.Keywords: Tamarind, Physicochemical properties, Minerals, Vinegar quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".