Nutrient Compositions and Total Polyphenol Contents of Selected Dried Fruits Available in Selangor, Malaysia
Bibliographic record
Abstract
Dried fruits contained higher amount of energy and nutrient density compared to the fresh ones. This study was conducted to determine and compare the content of nutrients and polyphenols in dried dates, raisin, apricot and fig. Dried fruit samples were bought from three different stores in Selangor and analyzed in triplicates. AOAC (1997) standard methods were used for proximate analyses. The total sugar and polyphenol contents were determined using Dubois and Folin-Ciocalteu methods, respectively. Highest moisture content was found in dried apricots (35.26%) while the lowest one was in Mariami dates (21.68%). Dried apricots also contained highest total ash content (4.54%) while Safawi dates have the lowest one (2.45%). Dried figs contained the highest crude protein (3.93%) and fat contents (4.02%) while Safawi dates have lowest protein (2.57%) and fat (0.09%) contents. Total carbohydrates were highest in Safawi dates (72.81%) and lowest in dried apricots (56.09%). Highest total sugar content found in Mariami dates (48.61%) and lowest in dried apricots (10.35%). Total phenolic content in golden raisin contained significantly (p < 0.001) highest polyphenol content (562.15 mg GAE/100 g) than others while dried figs have the lowest one (151.04 mg GAE/100 g). Dried fruits analyzed in this study contained high nutrients and polyphenol contents which are suitable to be consumed as an alternative for snack, with a caution on the portion size due to the high sugar content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".