Quantification of beef, pork, chicken and turkey proportions in sausages: use of matrix-adapted standards and comparison of single versus multiplex PCR in an interlaboratory trial

Quantification of Pork, Chicken, Beef, and Sheep Contents in Meat Products Using Duplex Real-Time PCR

Accurate methods for meat speciation and quantification are essential for ensuring the supply of safe and wholesome meat and composite products with animal origins to negate the potential associated hazards, aid classification of consignments at the import control system, and thwart food fraud committed for financial gain. To better enhance meat safety control and combat food fraud, this study developed two duplex real-time polymerase chain reaction (real-time PCR) systems specifically designed for chicken, pork, sheep, and beef, using single-copy, chromosomally encoded, species-specific gene sequences to accurately measure the content of each meat type in meat products. DNA extracted from the raw and boiled reference materials prepared in varying proportions (ranging from 1% to 75%) were used in the development of the duplex assay to derive calibration factors to determine the meat content in different meat products. The method was further validated using proficiency test samples and market monitoring samples. Our findings showed that this method exhibits high specificity and sensitivity, with a significant accuracy range of 0.14% to 24.07% in quantifying the four meat types in both raw and processed meat products. Validation results further confirmed the effectiveness of our method in accurately quantifying meat content. Thus, we have demonstrated the duplex qPCR assays as promising approaches for implementation in routine analysis to strengthen meat safety control systems and combat meat fraud, thereby safeguarding consumer health and trust in the meat industry.

Identification of Mammalian and Poultry Species in Food and Pet Food Samples Using 16S rDNA Metabarcoding

The substitution of more appreciated animal species by animal species of lower commercial value is a common type of meat product adulteration. DNA metabarcoding, the combination of DNA barcoding with next-generation sequencing (NGS), plays an increasing role in food authentication. In the present study, we investigated the applicability of a DNA metabarcoding method for routine analysis of mammalian and poultry species in food and pet food products. We analyzed a total of 104 samples (25 reference samples, 56 food products and 23 pet food products) by DNA metabarcoding and by using a commercial DNA array and/or by real-time PCR. The qualitative and quantitative results obtained by the DNA metabarcoding method were in line with those obtained by PCR. Results from the independent analysis of a subset of seven reference samples in two laboratories demonstrate the robustness and reproducibility of the DNA metabarcoding method. DNA metabarcoding is particularly suitable for detecting unexpected species ignored by targeted methods such as real-time PCR and can also be an attractive alternative with respect to the expenses as indicated by current data from the cost accounting of the AGES laboratory. Our results for the commercial samples show that in addition to food products, DNA metabarcoding is particularly applicable to pet food products, which frequently contain multiple animal species and are also highly prone to adulteration as indicated by the high portion of analyzed pet food products containing undeclared species.

Real-Time PCR Assay for the Detection and Quantification of Roe Deer to Detect Food Adulteration-Interlaboratory Validation Involving Laboratories in Austria, Germany, and Switzerland

Game meat products are particularly prone to be adulterated by replacing game meat with cheaper meat species. Recently, we have presented a real-time polymerase chain reaction (PCR) assay for the identification and quantification of roe deer in food. Quantification of the roe deer content in % (w/w) was achieved relatively by subjecting the DNA isolates to a reference real-time PCR assay in addition to the real-time PCR assay for roe deer. Aiming at harmonizing analytical methods for food authentication across EU Member States, the real-time PCR assay for roe deer has been tested in an interlaboratory ring trial including 14 laboratories from Austria, Germany, and Switzerland. Participating laboratories obtained aliquots of DNA isolates from a meat mixture containing 24.8% (w/w) roe deer in pork, roe deer meat, and 12 meat samples whose roe deer content was not disclosed. Performance characteristics included amplification efficiency, level of detection (LOD_95%), repeatability, reproducibility, and accuracy of quantitative results. With a relative reproducibility standard deviation ranging from 13.35 to 25.08% (after outlier removal) and recoveries ranging from 84.4 to 114.3%, the real-time PCR assay was found to be applicable for the detection and quantification of roe deer in raw meat samples to detect food adulteration.

Big Data in metagenomics: Apache Spark vs MPI

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.

Comparison of Real-Time PCR Quantification Methods in the Identification of Poultry Species in Meat Products

Poultry meat is consumed worldwide and is prone to food fraud because of large price differences among meat from different poultry species. Precise and sensitive analytical methods are necessary to control poultry meat products. We chose species-specific sequences of the cytochrome b gene to develop two multiplex real-time polymerase chain reaction (real-time PCR) systems: one for chicken (Gallus gallus), guinea fowl (Numida meleagris), and pheasant (Phasianus colchicus), and one for quail (Coturnix japonica) and turkey (Meleagris gallopavo). For each species, added meat could be detected down to 0.5 % w/w. No cross reactions were seen. For these two real-time PCR systems, we applied three different quantification methods: (A) with relative standard curves, (B) with matrix-specific multiplication factors, and (C) with an internal DNA reference sequence to normalize and to control inhibition. All three quantification methods had reasonable recovery rates from 43% to 173%. Method B had more accepted recovery rates, i.e., in the range 70-130%, namely 83% compared to 75% for method A or C.

A big data approach to metagenomics for all-food-sequencing

BACKGROUND

All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches.

RESULTS

We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark).

CONCLUSIONS

We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).

Development and validation of a fallow deer (Dama dama)-specific TaqMan real-time PCR assay for the detection of food adulteration

The aim of the present study was to develop a real-time PCR assay for the identification and quantification of fallow deer (Dama dama) in food to detect food adulteration. Despite high sequence homology among different deer species, a fallow deer-specific primer/probe system targeting a fragment of the nuclear MC1-R gene was designed. This primer/probe system did not amplify DNA from 19 other animals and 50 edible plant species. Moderate cross-reactivity was observed for sika deer, red deer, roe deer, reindeer and wild boar. The LOD and LOQ of the real-time PCR assay were 0.1% and 0.4%, respectively. To validate the assay, DNA mixtures, meat extract mixtures, meat mixtures and model game sausages were analyzed. Satisfactory quantitative results were obtained when the calibration mixture was similar to the analyzed sample in both the composition and concentration of the animal species of interest.

Quantitative detection of pork in commercial meat products by TaqMan® real-time PCR assay targeting the mitochondrial D-loop region

The TaqMan® real-time PCR assay using the mitochondrial D-loop region was developed for the quantitative detection of pork in processed meat products. The newly designed primers and probe specifically amplified pork without any cross-reactivity with non-target animal species. The limit of detection of the real-time PCR assay was 0.1pg of heat-treated pork meat and 0.1% (w/w) pork meat in beef and chicken meat mixtures. The quantitative real-time PCR assay was applied to analyze the pork meat content in 22 commercial processed meat products including jerkies, press hams, sausages, hamburger patties and steaks, grilled short rib patties, and nuggets. The developed real-time PCR method was able to detect pork meat in various types of processed meat products that declared the use of pork meat on their label. All processed meat products that declared no use of pork meat showed a negative result in the assay. The method developed in this study showed sensitivity and specificity in the quantification of pork meat in commercial processed meat products.

Authenticity control of game meat products--a single method to detect and quantify adulteration of fallow deer (Dama dama), red deer (Cervus elaphus) and sika deer (Cervus nippon) by real-time PCR

This contribution presents a single real-time PCR assay allowing the determination of the deer content (the sum of fallow deer (Dama dama), red deer (Cervus elaphus) and sika deer (Cervus nippon)) in meat products to detect food adulteration. The PCR assay does not show cross-reactivity with 20 animal species and 43 botanical species potentially contained in game meat products. The limit of quantification is 0.5% for fallow deer and red deer and 0.1% for sika deer. The deer content in meat products is determined by relating the concentration obtained with the deer PCR assay to that obtained with a reference system which amplifies mammals and poultry DNA. The analysis of binary meat mixtures with pork, a meat mixture containing equal amounts of fallow deer, red deer and sika deer in pork and a model game sausage showed that the quantification approach is very accurate (systematic error generally <25%).

All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing

BACKGROUND

DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components.

RESULTS

Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens.

CONCLUSIONS

Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.

A nuclear DNA-based species determination and DNA quantification assay for common poultry species

DNA testing for food authentication and quality control requires sensitive species-specific quantification of nuclear DNA from complex and unknown biological sources. We have developed a multiplex assay based on TaqMan® real-time quantitative PCR (qPCR) for species-specific detection and quantification of chicken (Gallus gallus), duck (Anas platyrhynchos), and turkey (Meleagris gallopavo) nuclear DNA. The multiplex assay is able to accurately detect very low quantities of species-specific DNA from single or multispecies sample mixtures; its minimum effective quantification range is 5 to 50 pg of starting DNA material. In addition to its use in food fraudulence cases, we have validated the assay using simulated forensic sample conditions to demonstrate its utility in forensic investigations. Despite treatment with potent inhibitors such as hematin and humic acid, and degradation of template DNA by DNase, the assay was still able to robustly detect and quantify DNA from each of the three poultry species in mixed samples. The efficient species determination and accurate DNA quantification will help reduce fraudulent food labeling and facilitate downstream DNA analysis for genetic identification and traceability.