NUTRITIONAL COMPOSITION OF VARIOUS PACKAGED DRIED CHIPS AVAILABLE IN PAKISTANI MARKET
Bibliographic record
Abstract
Background: Dried chips are among the most commonly consumed snacks in Pakistan not only by children but also by adults. Despite the fact that they are consumed in large amounts, they pose several health hazards. The major objective of the present research was to determine the nutritional composition of various packaged dried chips available in the local market.Methodology: Eight most sought after brands of dried chips were chosen and analyzed with regards to their nutritional composition.Results: The results showed that one serving of dried chips (30 grams) provided 149.62 ± 19.10 calories. Moreover, one serving of dried chips contributed to 14.99 ± 4.14 grams of carbohydrates while 2.05 ± 3.63 grams of sugars. Fats provided by one serving of chips were calculated to be as 7.40 ± 3.63 grams. An alarming figure was that only one serving of dried chips contributed to 3.44 ± 1.94 grams of saturated fats. Moreover, a serving of chips provided 1.99 ± 0.82 grams of protein while only 0.82 ± 0.57 grams of fiber. Likewise, a high amount of sodium per serving was seen to be contributed by a serving of chips, which was 205.60 ± 37.43 mg.Conclusions: The study demonstrated that the dried chips were not high in calories but also contributed to lots of carbohydrates and saturated fats. Moreover, the fiber content provided by chips was almost negligible.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".