In-silico approaches for discrimination of Curcuma species and their closely related family using the novel technique of DNA Barcoding
Bibliographic record
Abstract
In this study, we have discriminated and identified the Genus of Curcuma and related species Zingiberaceae using rbcL and trnL DNA barcode primers. Curcuma genus related to the family Zingiberaceae comprises a significant number of medicinal plants renowned for their use in ethnomedicine, playing a pivotal role in the medical, health, and pharmaceutical sectors. Traditionally, morphological methods alone have proven insufficient for accurately identifying species within this family. However, DNA barcoding technology provides a contemporary solution by utilizing plant DNA sequences for species identification, thus enabling effective conservation efforts. We used DNA barcoding techniques and for analysis used the Maximum Parsimony tree in MEGA 11 with the Kimura 2-parameter (KP model) to analyse the genetic relationships between species. Out of the 13 accessions that were studied, 12 accessions belonged to Curcuma caesia and 1 accession belonged to Curcuma aeruginosa. The genetic relationships observed were correlated with the geographical distributions of these species. It was determined that C. aeruginosa is a mutated species of C. caesia. Additionally, 1 specimen of Alpinia galanga, a plant species related to the Zingiberaceae. Barcode primer trnL primer demonstrated a 92% efficiency during the investigation. The rbcL and trnL loci are recommended as potential barcode markers for discriminating between different plant species. This study developed a comprehensive DNA barcoding database that can confidently differentiate between species by combining morphological and molecular data. This database has the potential to identify adulteration in herbal products, combat illegal trade and adulteration of plant species, and assist in germplasm conservation efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".