A Poultry Universal Primer-Based Fluorescent PCR (PUP-fPCR) for Simultaneous Identification and Quantification of Chicken, Quail, Duck, and Goose Meat Species

To combat poultry meat adulteration, we developed a poultry universal primer-based fluorescent PCR (PUP-fPCR). Through comprehensive genomic alignment analysis, a poultry-specific nuclear DNA sequence containing phylogenetically conserved regions and hypervariable segments with interspecies nucleotide polymorphisms was employed to develop universal primers targeting conserved flanking sequences and TaqMan probes for hypervariable segments. Then, a multiplex quantitative PCR method incorporating universal primers with four TaqMan probes was developed with high specificity and sensitivity (limit of detection: 0.005 ng). Analytical performance evaluation using prepared DNA mixtures revealed robust accuracy (relative deviation: 0.80–5.05%) and precision (relative standard deviation: 0.94–13.84%). This single-tube multiplex system leverages the spectral discrimination of TaqMan probes to simultaneously detect four poultry species, overcoming primer competition issues inherent in conventional multiplex PCR designs. This integrated approach reduces system complexity while maintaining detection efficiency, providing regulatory agencies with a robust tool for combating meat adulteration and ensuring food quality supervision.

Authors

Institutions

Publication Details

Journal
Molecules
Published
2026-05-09
DOI
https://doi.org/10.3390/molecules31101590
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Poultry Universal Primer-Based Fluorescent PCR (PUP-fPCR) for Simultaneous Identification and Quantification of Chicken, Quail, Duck, and Goose Meat Species

Jiaqi Yang, Guangxiang Chen, Haoyang Cao, X. Wang et al.
Molecules
Identification and Quantification in Food
article

A Poultry Universal Primer-Based Fluorescent PCR (PUP-fPCR) for Simultaneous Identification and Quantification of Chicken, Quail, Duck, and Goose Meat Species

Jiaqi Yang, Guangxiang Chen, Haoyang Cao, X. Wang, Rongyan Zhou, Mengyao Zhang, Yifan Li, Qiyue Yang, Wenjun Wang
article en

Abstract

To combat poultry meat adulteration, we developed a poultry universal primer-based fluorescent PCR (PUP-fPCR). Through comprehensive genomic alignment analysis, a poultry-specific nuclear DNA sequence containing phylogenetically conserved regions and hypervariable segments with interspecies nucleotide polymorphisms was employed to develop universal primers targeting conserved flanking sequences and TaqMan probes for hypervariable segments. Then, a multiplex quantitative PCR method incorporating universal primers with four TaqMan probes was developed with high specificity and sensitivity (limit of detection: 0.005 ng). Analytical performance evaluation using prepared DNA mixtures revealed robust accuracy (relative deviation: 0.80–5.05%) and precision (relative standard deviation: 0.94–13.84%). This single-tube multiplex system leverages the spectral discrimination of TaqMan probes to simultaneously detect four poultry species, overcoming primer competition issues inherent in conventional multiplex PCR designs. This integrated approach reduces system complexity while maintaining detection efficiency, providing regulatory agencies with a robust tool for combating meat adulteration and ensuring food quality supervision.

MoleculesVol. 31(10)
Hebei Agricultural University (CN)
Openalex Percentile: Top 7%
Identification and Quantification in Food
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.