Analysis of Volatile Organic Compounds
Bibliographic record
Abstract
In this study, GC-MS and sensory evaluation were used to analyze the volatile components of the fruits of 2 white M. rubra and 2 purple M. rubra . Meanwhile, their relationship among each other and massion pine ( Pinus massoniana ) were detected. The GC-MS test results showed that, there were 60 volatile organic compounds (VOCs) in 8 categories, including aldehydes, acids, ketones, alcohols, esters, phenols, olefins and alkanes, found in the four M. rubra cultivars. Among which, 32 were found in M. rubra cv “Biqi”, and 25 were found in M. rubra cv “Yongjia”. 18 were both found in M. rubra cv “Shangyu” and M. rubra cv “Yewu”. The three main volatile components of the four M. rubra were different, among which, Yongjia was mainly composed of turpentine-4-alcohol, 1-methyl-4-(1-methyl-ethyl) cyclohexene and turpentinene, accounting for 53.30%. Yewu was caryophyllene, L-turpinol and D-limonene, accounting for 53.77%. Shangyubaiyangmei was composed of pinole-4-alcohol, pinolene, 1,3,8-p-menthotriene, accounting for 61.37%. Biqi were caryophyllene, ethyl acetate and o-isopropyl toluene, accounting for 73.22%. The main components of masson pine resin were α-pinene, camphene, β-pinene, which were different with M. Myrica . At the same time, ISSR molecular marker analysis of bayberry leaves and pine needles showed that Biqi had close relationship with Yewu, Shangyubaiyangmei had close relationship with Yongjia bayberry, but not with Masson's pine. Therefore, it is suggested that the pine aroma of M. Myrica fruit may be the characteristic expression form in the evolutionary process, which is related to the genetic evolution and breeding results, and has nothing to do with masson pine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".