Impacts of phenolic compounds on RT-qPCR detection of hepatitis A virus in berries
Bibliographic record
Abstract
Berries are frequently implicated in outbreaks of foodborne illness due to viruses, particularly norovirus and hepatitis A virus. Compounds naturally present in berries can compromise the reliability of RT-qPCR methods, such as ISO 15216-1:2017, for detecting and quantifying viruses in foods. The aim of this study was to evaluate the inhibitory impact of seven phenolic compounds (ellagic acid, hydroxybenzoic acid, caffeic acid, coumaric acid, ferulic acid, quercetin, and cyanidine-3-glucoside) found naturally in raspberries as well as batch effects due to different concentrations of inhibitors (e.g., associated with ripeness) when using RT-qPCR to detect HAV in raspberries, blackberries, strawberries, blueberries, cranberries, and mixed berries. To assess the impact of dilution on RT-qPCR inhibition, samples were diluted at four levels (1/2, 1/5, 1/10, 1/100). Spiking the RT-qPCR reaction mixture with each phenolic compound at its natural concentration in raspberries showed that ellagic acid, hydroxybenzoic acid, caffeic acid and cyanidin-3-glucoside inhibited amplification, but only ellagic acid remained inhibitory in the ISO method. HAV recovery from frozen strawberries was undetectable (0 %) without additional treatment but reached 39 % with the OneStep PCR Inhibitor Removal Kit. For frozen blueberries, MobiSpin S-400 performed better, yielding about 52 % recovery compared to 23 % with OneStep. Sample dilution further enhanced HAV detection across most berry types.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".