Identification of Secondary Metabolites in Citrus Fruit Using Gas Chromatography and Mass Spectroscopy
Bibliographic record
Abstract
This experiment targets undergraduate students in an analytical or organic instructional context. Using a simple extraction, this protocol allows students to quantify and qualify monoterpenes in essential oils from citrus fruit peels. The procedures involve cooling down the peels by immersing them into icy water. After a few minutes, the chilled peels are pulped in a simple kitchen blender using acidic brine to hydrolyze the undesired fatty acids. Essential oils are extracted from the emulsion using methylene chloride and are then injected in a gas chromatograph coupled with a mass spectrometer. Among the fruit tested—limes, grapefruits, and oranges—all showed a high concentration of ( R )-limonene, a monoterpenoid commonly found in these fruits. Students are invited to quantify ( R )-limonene in the extracts following an accurate 5-point standard calibration curve. For students, this experiment may be a first contact with the analysis of plant extracts as well as an introduction to the biochemistry of monoterpenes.
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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 | 0.001 | 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) | 0.001 | 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".