Nutritional Assessment of Pulps and Partial Characterization of Seed Oils from Varieties of Pear Fruits
Bibliographic record
Abstract
The nutrients and chemical contents of Persea americana, Dacryodes edulis, and Canarium scheinfurthi fruits and partial characterization of their seed oils were carried out to ascertain their nutritional benefits. The fruit pulps were analyzed for chemical (proximate) composition, amino acids profile, vitamins, and phytochemical and anti-nutritional compositions. Oils were extracted from the fruit seeds and the physico-chemical properties of the seed oils were determined according to the standard protocols. The results showed that the fruit pulps contained an abundance of macro- and micro-nutrients which varied significantly (p < 0.05) among the varieties with low anti-nutrients. The essential amino acid contents were high and varied significantly (p < 0.05) among the varieties. Glutamic acid, followed by aspartic acid had the highest concentration of the amino acids, while the concentrations of methionine and cysteine were low in all the varieties. The results also revealed high essential amino acids score values, above 100% for isoleucine and total aromatic amino acids. The physicochemical properties of the fruit oils showed that the oils were edible (low acid value) and may have industrial potential due to their low peroxide, iodine, and saponification values. Overall, the results showed that the pears are nutritionally rich and could serve dietetic and industrial purposes.
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.
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".