Quality of Minimally Processed Products Marketed in Cuiabá, Mato Grosso, Brazil
Bibliographic record
Abstract
Consumers have been increasingly seeking healthier foods without sacrificing sensory satisfaction and convenience, which are highly acclaimed attributes in modern times. Minimally processed products can meet these demands. The present study evaluated the microbiological, microscopic and physicochemical quality of minimally processed fruits and vegetables marketed in Cuiabá, Brazil. A total of 36 samples, consisting of sliced melon, fruit salad in pieces, grated carrot, diced melon, sliced papaya and pineapple rounds were subjected to microbiological, microscopic and physicochemical analyses. Salmonella spp. was absent in all samples, while 27.8% (10/36) of the samples showed coliform counts at 45 °C over 2.0 and 2.7 log CFU.g-1 in fruit and vegetables, respectively. Light dirt and foreign matter were present in 55.5% of the samples (20/36), including wood fragments and insect/animal excrements (rat hair) in 13.9% (5/36) of the samples. The pH and soluble solids (°Brix) results ranged from 3.84 to 6.66 and from 8.19 to 10.24, respectively. The products were in different stages of maturation and 27.8% (10/36) of the sliced papaya and grated carrots were in unsatisfactory sanitary conditions under the current Brazilian legislation.
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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 | 0.001 |
| 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".