Role of chemometric classification for future prediction: application on differentgeographical origins of Jordanian Guava
Bibliographic record
Abstract
In this study, the guava-origin fruits were collected from different cultivated regions in Jordan, then scanned using gas chromatography–mass spectrometry (GC-MS) to reveal the chemical constituents. The chemical contents were then used with the help of multivariate analysis to classify the regions. Guava fruit was collected from; Northern Shouneh-1, Northern Shouneh-2, Madaba, Saham Al-Kfarat, and Southern Shouneh. Hydrodistillation was implemented to extract the essential oils from guava fruits. Comprehensive chemical profiling of the extracted essential oils was achieved using (GC -MS). A total of thirty-eight chemical compounds have been detected and identified with variances from one region to another. Limonene, longifolene, β-copaene, and t-muurolo were found in high concentrations among the other detected compounds. The GC-MS data were subjected to Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to reveal the similarities/differences between guava fruit regions. The Northern Shouneh-1 and Madaba regions' fruits showed high similarity to each other due to the distinct contents of limonene and longifolene. On the other hand, cadinol was the main compound in Saham Kfarat and Southern Shouneh regions. Finally, Northern Shouneh-2 guava samples showed different content than other regions due to the distinguished levels of t-muurolol. Guava classification based on the GC-MS profile will meet the practical needs of its applications in food production and will contribute to the standardization of commercially available cultivars in Jordan.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".