Investigating appropriate molecular and chemical methods for ingredient identity testing of plant-based protein powder dietary supplements
Bibliographic record
Abstract
Plant-based protein powders are rapidly growing in popularity, and outdated quality assurance tools expose vulnerabilities to adulteration via different methods of "protein spiking". Adequate diagnostic tools are urgently needed to be able to authenticate protein source ingredients and screen for potential adulterants. We explored the application of three diagnostic tools for ingredient identification: targeted PCR with Sanger sequencing, NGS, and LC-MS/MS. We collected 33 samples of common commercial products from the plant-based protein powder market and sought to identify botanical components using the three technologies. We found success in detection with all approaches, with at least one main protein source being identified by at least one approach in all samples. The investigation uncovered challenges to data collection or result interpretation with each technology including but not limited to amplification biases with PCR technologies, potential influence of DNA degradation, and issues with protein solubility during isolation. Ultimately, each platform demonstrated utility along with certain caveats, which epitomized the importance of orthogonality of testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".