Macronutrient Composition, Functional and Textural Properties of Selected Traditional Sweetmeats of Sri Lanka
Bibliographic record
Abstract
Purpose: Sri Lankan traditional sweetmeats occupy a special place in regular consumption, festivities and religious offerings. Sweetmeats are popular food items since ancient times, however, their compositional information are limited. The objective of this study is to provide information on macro-nutrients, energy intake, antioxidant potential and bioactive compounds of selected sweetmeats. Research method: Twenty-five sweetmeat prepared with standardized recipes were analyzed for major nutrients using standard analytical methods. Methanol (80%, v/v) extracts of these products were evaluated for antioxidant potential (AP) by Ferric Reducing Antioxidant Potential (FRAP), 2,2-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) scavenging activity and 2,2′-diphenyl-1-picrylhydrazyl radical (DPPH) radical scavenging assays. Findings: Almost all tested sweetmeats were energy-dense foods. Among the deep-fried foods, Beraliya kevum had the highest fat content 28.23±1.06 %. Kos eta aggala (68.05±1.30%) reported the highest carbohydrate content and Unduwalalu had the highest protein content (8.70±0.33%) among all the sweetmeats. The AP of Hal helapa made of rice flour, finger millet flour and Vateria copallifera was significantly (p<0.05) higher compared to all other sweetmeats; 222.44±5.34 mM TEAC/g dry matter by DPPH assay and 240.28±5.62 mM TEAC/g dry matter by ABTS assay. Stable polyphenolic compounds and Maillard reaction products generated during high temperatures of processing may be contributing to high AP. Originality/Value: These findings are useful to enhance the consumer awareness in making food choices based on the major nutrients and antioxidant potential. These data can be used to improve the health-related parameters of traditional sweetmeats by reformulating with healthy ingredients and meeting the health concerns of consumers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".