RETRACTED: Effect of derivatization method (KOH and BF3) on fatty acid profile data of boiled Tetracarpidium conophorum, and egusi pudding oils
Post-publication record
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Bibliographic record
Abstract
Fatty acids are present in many foods, either free or esterified. Their presence helps to characterize and classify the food. The nature of these fatty acids is also associated with the treatments applied. To assess the fatty acid profile of these matrices, extractions are carried out using different solvents that influence the nature and lipid profile. The subsequent derivatization of fatty acids to more volatile fatty acyl methyl esters (FAMEs) prior to determination of the fatty acid profile takes into account the nature of the extraction solvent. Thus, the present work proposes to determine the fatty acid profile by Gas Chromatography Flame Ionisation Detector (GC-FID) of two lipid extracts derivatized by the MeOH/KOH and Hexane/MeOH/MeOH-BF3 procedures. Freshly harvested Tetracarpidium conophorum nuts from fields in the Fombap locality were brought to the laboratory where they were boiled (95°C; 30 min), shelled, cut into small cubes and dried for 48 h at 45°C. The dried seeds were ground and the resulting paste macerated in hexane for 48 h. The liquid fraction obtained was concentrated using a rotavapor, and the lipid extracts were stored at -15°C. The egusi pudding was obtained by mixing 100 g of egusi seed paste with 0.50 g of white Piper nigrum powders, then packed in bulrush leaves and steamed for 120 min. After cooking, the product was stored for 4 days at room temperature and reheated twice a day. At the end of the last day, the lipid fraction oil was extracted following the methodology of Bligh and Dyer [1], then concentrated and preserved as before. The lipid extracts were then methylated using MeOH/KOH and Hexane/MeOH/MeOH-BF3 methods before injection into a GC-FID equipped with a Stabil Wax®-DA column. Supelco's standard mix of 37 FAMEs was used to identify and quantify the fatty acids present in the various samples. The results obtained enable us to identify the different fatty acids according to the retention time of their corresponding FAMEs and to quantify them. The fatty acids obtained were classified as saturated and unsaturated (mono and polyunsaturated). These analyses showed that the rapid derivatization method (MeOH/KOH) identified the same number of fatty acids as the Hexane/MeOH/MeOH-BF3 method in the lipid extract from the egusi pudding, whereas the Hexane/MeOH/MeOH-BF3 method identified four more fatty acids in the lipid extract coming from Tetracarpidium conophorum. Although the number of fatty acids was similar, the derivatization method influenced the nature of the fatty acids in the egusi pudding lipid extract. Overall, polyunsaturated fatty acids were the most abundant in the different oils. Omega-3 were the majority subclass in Tetracarpidium conophorum nuts, while omega-6 were in egusi pudding. The derivatization method did not influence the majority fatty acid (alpha linolenic) in Tetracarpidium conophorum nuts, whereas derivatization with BF3 gave trans linoleic and KOH cis linoleic in egusi pudding. These results show that the choice of derivatization method for fatty acid profiling and quantification is very important and depends on the technique and extraction solvents used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".