Identification of the Constituents of Balsam of Peru in Tomatoes
Bibliographic record
Abstract
BACKGROUND: Studies show that balsam-restricted diets result in significant improvement of systemic contact dermatitis in patients with contact allergy to balsam of Peru (BOP). While tomatoes have been implicated as a frequent cause of BOP-related dermatitis, the presence of BOP in tomatoes has never been confirmed. OBJECTIVES: High-performance liquid chromatography coupled with mass spectrometry (liquid chromatography [LC]-MS) and UV spectrometry (LC-UV) was used to detect the possible presence of BOP constituents in tomatoes. METHODS: Samples of beefsteak, cherry, and plum tomatoes were extracted in ethyl acetate and analyzed with LC-MS and LC-UV for the presence of the following sensitizing constituents of BOP: benzoic acid, benzyl alcohol, trans-cinnamic acid, cinnamic alcohol, cinnamyl cinnamate, coniferyl alcohol, eugenol, isoeugenol, and methyl cinnamate. RESULTS: The initial LC-MS analysis of each tomato extract showed multiple peaks. Two of these peaks had molecular weights of 134 and 180, which correspond to cinnamic alcohol and coniferyl alcohol, respectively. The analysis did not show peaks corresponding to the molecular weights of the remaining compounds. Cochromatography of tomato extract with cinnamic alcohol and coniferyl alcohol using LC-UV further suggested the presence of these compounds in the tomato extract. CONCLUSION: Coniferyl alcohol and cinnamic alcohol, constituents of BOP, are present in beefsteak, cherry, and plum tomatoes.
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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) | 0.001 | 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) | 0.002 | 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".