Characterization of oils and defatted residues of <i>Terminalia catappa</i> L. seed kernels of two varieties
Bibliographic record
Abstract
The seed kernel of Terminalia catappa Linn (T. catappa) is an underutilized plant food with promising potential. This study investigated the physicochemical properties, fatty acid composition, thermal behavior, and Fourier transform infrared (FTIR) spectral characteristics of oils extracted from kernels of yellow and purple cultivars of T. catappa and proximate compositions of their defatted residues. The oils extracted through a cold press micro-expeller, differed in color, with yellow oil being lighter than purple oil. Both cultivars demonstrated high iodine values and lower saponification values. Thermal profiles displayed major exothermic and endothermic peaks associated with the crystallization and melting of triacylglycerols (TAGs). Both oils were rich in unsaturated fatty acids (USFAs), particularly oleic and linoleic acids, with palmitic acid being the predominant saturated fatty acid (SFA). FTIR spectra indicated the presence of functional groups such as methyl, methylene and esters representing the complex composition of the oils. Proximate composition analysis revealed that whole kernels were high in fat, while defatted residues were richer in protein and minerals. These findings suggest that T. catappa kernels from both cultivars were good sources of plant oils with potential for high-fat products, and defatted residues could be used in protein-rich supplements, offering diverse industrial applications.
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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 | 0.000 | 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 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".