Effects of Boiling and Smoking on the Proximate Composition and Oil Quality of a Commercially Important Freshwater Fish (Chrysichthys nigrodigitatus) from Nkam River in Cameroon
Bibliographic record
Abstract
The effects of boiling and smoking on the proximate composition and lipids quality of a freshwater fish (Chrysichthys nigrodigitatus) collected from Nkam River in Cameroon were investigated. Fresh fish was filled, boiled or smoked and then, aliquoted for analyses. One portion was dried at 50 °C and ground into flour for the proximate composition determination. The other portion was served for lipids extraction using the Bligh and Dyer method. The extracted oil was characterized physico-chemically using chemical indexes and GC/FID. Moisture, protein, ash and lipid contents of raw fish were 80.67±4.18, 64.42±1.51, 10.90±0.42 and 22.06±5.40 % dm respectively. The changes in dry matter, protein and ash contents were found to be statistically significant (P<0.05) after smoking. The most important mineral of this fish was the Potassium (7017.54-8771.93 mg/kg). Except the calcium and phosphorus contents which decreased with the treatments, the amount of the other detected minerals was significantly increased. It was also found that these technological treatments significantly increased (P<0.05) the free fatty acids and hydroperoxides formation in oil, while it decreased its iodine value. The fatty acid profile of untreated and treated fish revealed her richness in Palmitic (22.91-34.76%), Oleic (12.83-23.55%), Stearic (11.29-14.81%), Linoleic (LA) (ω6) (2.83-6.75%), Arachidonic (ω6) (2.50-6.64%) and Docosahexaenoic (DHA) (ω3) (1.56-12.31%) acids. The ratio PUFA/SFA of raw fish (0.47) was severely reduced by the smoking (0.26) while it increases after boiling (0.61). This fish contain appreciable levels of Omega-6 PUFA, suggesting that it could be used as a source of healthy diet for human beings. These findings may also be beneficial for the fish industry, nutritionists and researchers who were striving to improve the nutritive value, processing and marketing of selected fish species.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".