Development of a multiplex real‐time PCR for simultaneous detection of <scp><i>Bacillus cereus</i></scp>, <scp><i>Listeria monocytogenes</i></scp>, and <scp><i>Staphylococcus aureus</i></scp> in food samples
Bibliographic record
Abstract
Abstract Bacillus cereus, Listeria monocytogenes, and Staphylococcus aureus caused problems in public health and food safety. A multiplex real‐time PCR (qPCR) for simultaneous detection of these three pathogens in different kinds of food was developed. A high specificity (100%) was obtained using 21 target strains and 19 nontarget strains. Standard curves for pure cultures covered seven orders of magnitude (from 108 to 102 cfu/ml) with high amplification efficiencies ranging from 94.2 to 105.4% with R‐squares over 0.999. When multiplex qPCR was applied for artificially contaminated cherry tomato, milk, and spam samples, a detection limit of 103 cfu/g or ml was obtained for these three bacteria. When low levels (0.4–5.5 cfu/25 g or ml) of bacteria were inoculated in three kinds of food samples and cultured in tryptic soy broth for 24 hr, results obtained from multiplex real‐time and conventional culture methods were not significantly different for all three food matrices based on Mantel–Haenszel chi‐square test. Only for B. cereus in milk, positive portions detected by qPCR were significantly higher than those detected by culture method. Hence, the multiplex qPCR developed in this study is highly specific and effective for simultaneous detection B. cereus, L. monocytogenes, and S. aureus in food samples. Practical applications Bacillus cereus, Listeria monocytogenes, and Staphylococcus aureus are three major foodborne pathogens and have been found in a wide range of foods. We developed a multiplex qPCR specific targeting groEL, iap, and nuc genes with a high amplification efficiency. Different food samples (cherry tomato, milk, and spam) were tested for evaluating the performance of the multiplex qPCR. The detection results by qPCR were compared with the conventional culture methods and no significant differences were found using Mantel–Haenszel chi‐square test. This study provides the information of the developed multiplex qPCR for detecting three specific foodborne pathogens and applications in food samples, which will be helpful for further studies about simultaneous detection of several targets in food samples using multiplex PCR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".