Validation of a Triplex Quantitative Polymerase Chain Reaction Assay for Detection and Quantification of Traditional Protein Sources, Pisum sativum L. and Glycine max (L.) Merr., in Protein Powder Mixtures
Bibliographic record
Abstract
Several botanicals have been traditionally used as protein sources, including the leguminous Pisum sativum L. and Glycine max (L.) Merr. While a rich history exists of cultivating these plants for their whole, protein-rich grain, modern use as powdered supplements present a new challenge in material authentication. The absence of clear morphological identifiers of an intact plant and the existence of long, complex supply chains behoove industry to create quick, reliable analytical tools to identify the botanical source of a protein product (many of which contain multiple sources). The utility of molecular tools for plant-based protein powder authentication is gaining traction, but few validated tools exist. Multiplex quantitative polymerase chain reaction (qPCR) can provide an economical means by which sources can be identified and relative proportions quantified. We followed established guidelines for the design, optimization, and validation of qPCR assay, and developed a triplex qPCR assay that can amplify and quantify pea and soy DNA targets, normalized by a calibrator. The assay was evaluated for analytical specificity, analytical sensitivity, efficiency, precision, dynamic range, repeatability, and reproducibility. We tested the quantitative ability of the assay using pea and soy DNA mixtures, finding exceptional quantitative linearity for both targets – 0.9983 (p < 0.0001) for soy and 0.9915 (p < 0.0001) for pea. Ratios based on mass of protein powder were also tested, resulting in non-linear patterns in data that suggested the requirement of further sample preparation optimization or algorithmic correction. Variation in fragment size within different lots of commercial protein powder samples was also analyzed, revealing low SD among lots. Ultimately, this study demonstrated the utility of qPCR in the context of protein powder mixtures and highlighted key considerations to take into account for commercial implementation.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".