LC-MS-based metabolomics for detecting adulteration in Tribulus terrestris-derived dietary supplements
Bibliographic record
Abstract
The widespread usage of Tribulus terrestris dietary supplements has elicited concerns over product authenticity and possible adulteration. This research utilized an untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics methodology to assess the composition of T. terrestris -derived supplements. Authentic plant materials , simulated adulterated samples, and commercial products were analyzed using principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and a convolutional neural network tool. The presence of PDE5 inhibitors and anabolic steroids in spiked samples was confirmed. Certain commercial products included undisclosed green tea and citrus-derived chemicals, likely incorporated to amplify stimulant effects and support testosterone-enhancing claims. Additionally, spirost-4-ene-3,12-dione was recognized as an indicator of possible steroidal saponin decomposition resulting from storage or processing conditions. This study illustrates the efficacy of LC-HRMS metabolomics in detecting supplement adulteration and emphasizes the need for rigorous quality control protocols to guarantee customer safety and product integrity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".