Bibliographic record
Abstract
The authenticity of botanical ingredients in marketed products has been the focus of widespread concern in the natural product and dietary supplement industry. Classically, plants are identified by physical examination of minimally processed biomass for diagnostic macroscopic and microscopic features. However, botanical ingredients in modern ingredient supply chains are harvested when floral parts are absent, utilize parts of the plant that lack flowers (e.g., roots) or are so highly processed that diagnostic anatomical features have been destroyed or removed (e.g., extracts). Chemical profiling of extracts with chemometric analyses shows promise as an approach for authenticating botanicals. Adulteration of ginseng ( Panax spp.) roots with Panax leaves is a common and attractive adulteration as leaves are considered useless by-products of ginseng cultivation despite having higher ginsenoside content than roots, thereby giving the false appearance of higher quality and potency. Chemical profiling by HPLC-UV shows unique chemical profiles for leaf versus root materials and using SIMCA it is possible to differentiate root from leaf and to detect high levels of adulteration. However using a new multivariate moving window PCA approach has allowed us to detect much lower levels of adulteration in products based on the PC score residuals of unknown samples. This represents a significant improvement over past statistical techniques that resulted in significantly improved detection limits compared with traditional regression models for detection of low level of contamination [1]. Acknowledgements: Funding provided by Canada Research Chairs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".