Chemical Composition and Functional Properties of Selected Seaweeds from the Kenya Coast
Bibliographic record
Abstract
The aim of the study was to determine the chemical and functional properties of five Kenyan seaweed species namely; (Hypnea musciformis, Eucheuma denticulatum, Laurencia intermedia, Sargassum oligocystum, Ulva fasciata) as a potential fat replacer in chicken sausage processing. The proximate composition was investigated using the standard AOAC methods, while the nitrogen-free extract (NFE) was determined by weight difference of the proximate components. The seaweeds were analyzed for mineral composition using atomic absorption spectrophotometry while the fatty acid profile was determined by gas chromatography. The water holding capacity and the emulsion capacity of the seaweed were determined using AACC procedures. The highest proximate component was NFE (65.06 %) while the least was crude fat (0.87 %). Among the nine minerals analyzed, calcium was the highest (1185.29 mg/100g) while lead was not detected. Saturated fatty acids (SFA) were the highest with a range of 53.03-71.05 % followed by monounsaturated fatty acids (4.83-17.71%) and polyunsaturated fatty acids (PUFA) (2.75 - 10.13%). The highest emulsifying activity was obtained in Ulva fasciata (75.66 %) and Eucheuma denticulatum (75.69 %) while the lowest was obtained in Sargassum oligocystum (59.19 %). The highest water holding capacity was obtained in Sargassum oligocystum (13.75ml/g) while the lowest was recorded in Eucheuma denticulatum (8.42ml/g) and Ulva fasciata (9.16ml/g). The findings of this study demonstrated the potential of seaweeds in improving the chemical and functional characteristics of processed foods.
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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) | 0.001 | 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".