Chemical Composition and Functional Properties of Selected Seaweeds from the Kenya Coast
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it