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Article

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

by
Laura Preckel
1,
Claudia Brünen-Nieweler
1,*,
Grégoire Denay
2,
Henning Petersen
3,
Margit Cichna-Markl
4,
Stefanie Dobrovolny
5 and
Rupert Hochegger
5
1
Chemical and Veterinary Analytical Institute Muensterland-Emscher-Lippe (CVUA-MEL), Joseph-Koenig-Strasse 40, 48147 Muenster, Germany
2
Chemical and Veterinary Analytical Institute Rhein-Ruhr-Wupper (CVUA-RRW), Deutscher Ring 100, 47798 Krefeld, Germany
3
Chemical and Veterinary Analytical Institute Ostwestfalen-Lippe (CVUA-OWL), Westerfeldstrasse 1, 32758 Detmold, Germany
4
Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 38, 1090 Vienna, Austria
5
Austrian Agency for Health and Food Safety (AGES), Institute for Food Safety Vienna, Department for Molecular Biology and Microbiology, Spargelfeldstrasse 191, 1220 Vienna, Austria
*
Author to whom correspondence should be addressed.
Foods 2021, 10(11), 2875; https://doi.org/10.3390/foods10112875
Submission received: 23 September 2021 / Revised: 12 November 2021 / Accepted: 15 November 2021 / Published: 20 November 2021
(This article belongs to the Special Issue Techniques for Food Authentication: Trends and Emerging Approaches)
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Abstract

:
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.

1. Introduction

Commercial food and feed products must meet the requirements of national and international regulations. Manufacturers have to ensure that their products are both safe and authentic. However, food fraud has become a global issue, with meat products being particularly vulnerable to adulteration [1]. The term food fraud encompasses a variety of activities that are committed intentionally and aimed at deceiving consumers with respect to food quality. Meat products are frequently found to be adulterated by substitution of animal species given on the label by animal species of lower commercial value [2].
Food controls play a crucial role in the mitigation of food fraud. For the differentiation of animal species in food products, various molecular methodologies have been developed, including protein- and DNA-based ones [2,3,4,5,6]. DNA-based methodologies make use of genetic variations between species, e.g., single nucleotide polymorphisms (SNPs), insertions and deletions. They target either species-specific fragments in nuclear DNA or conserved regions in the mitochondrial genome. At present, DNA arrays and real-time PCR assays are mainly used for the authentication of meat products in official food laboratories.
DNA arrays are based on DNA hybridization [7]. In a first step, the target region, e.g., a conserved region of 16S rDNA, is amplified using biotinylated primers, resulting in the formation of biotinylated PCR products. The labeled PCR products are hybridized to species-specific oligonucleotide probes prespotted on a chip. After removing unbound PCR products by washing, hybridized PCR products are detected enzymatically. Commercial DNA arrays for animal species differentiation are fast, robust and cost-efficient [7]. They allow the simultaneous detection of the most relevant mammalian and poultry species for human consumption. Depending on sample matrix and processing grade, the limit of detection (LOD) ranges from 0.1% to 1%. A disadvantage of DNA arrays is that they do not yield quantitative information.
This limitation can be overcome by performing real-time PCR. However, quantification of animal species in meat products by real-time PCR is known to be a challenging task [1,3]. The main problem is to evaluate the meat content (w/w) one is actually interested in from the DNA concentration (e.g., ng/µL) determined by real-time PCR. Differences in tissue type, the number of cells per unit of mass, genome size, processing grade, and DNA extractability may impair the accuracy of quantitative results [8]. Various strategies have been proposed to compensate for these differences, e.g., the use of matrix-specific calibrators [9,10,11]. However, this strategy is very labor and time consuming. Thus, normalization with DNA extracts from material of defined composition [12] and relative quantification by using a reference real-time PCR assay [13,14,15] are widely applied in food control laboratories. With both approaches, the DNA ratios of the respective animal species in samples are obtained. Multiplex real-time PCR assays allow the identification of multiple species simultaneously, e.g., cattle, pig, turkey and chicken [16]; cattle, pig, equids and sheep [11]; roe deer, red deer, fallow deer and sika deer [17]; chicken, guinea fowl and pheasant or quail and turkey [18]. However, the number of species that can be targeted simultaneously is limited by the number of optical channels of the real-time PCR instrument.
In recent years, remarkable progress has been made towards developing DNA barcoding and DNA metabarcoding methods for food authentication [19,20,21,22,23]. DNA barcoding is based on amplification of short DNA barcode regions, followed by either high resolution melting (HRM) analysis [24,25] or Sanger sequencing [26,27]. DNA metabarcoding is the processing of multiple DNA templates using next-generation sequencing (NGS) technologies. While DNA barcoding via Sanger sequencing can only be applied for single species products, DNA metabarcoding also enables the identification of species in complex food and feed products containing multiple species. After amplifying the DNA barcode region, all amplicons, even those obtained for different samples, are sequenced in parallel. Finally, reads are analyzed using a bioinformatic workflow and compared to DNA reference sequences from well-characterized species for taxonomic assignment.
We have recently developed a DNA metabarcoding method allowing the identification of 15 mammalian and six poultry species [28]. The applicability of the method targeting a region of 16S rDNA was investigated by analyzing DNA extract mixtures and model sausages. The species of interest could be identified, differentiated and detected down to a proportion of 0.1%.
In the present study, we aimed at investigating the applicability of the DNA metabarcoding method for routine analysis in more detail. The design parameters and objectives of our study were as follows:
  • The study included 25 reference samples with known composition, 56 commercial food and 23 pet food products.
  • All samples were analyzed by the DNA metabarcoding method published previously [28] as well as by a commercial DNA array and/or by real-time PCR.
  • Qualitative and quantitative results obtained by DNA metabarcoding were compared to those obtained by the two PCR methodologies currently playing the most important role in meat species authentication in official food laboratories.
  • A subset of seven reference samples was analyzed by using the DNA metabarcoding method in two independent laboratories, yielding information on the robustness and reproducibility of the method.
  • We evaluated whether the results obtained by DNA metabarcoding were in line with sample composition (reference samples) or declaration (commercial food and pet food products).

2. Materials and Methods

2.1. Samples

For this study, a collection of various samples was compiled. Reference samples, comprising eight meat mixtures (LGC7240-49), four dairy products (DLA45 1–4) and 13 boiled sausages (DLA44, DLAptAUS2, Lippold A–C 2019–2021), were supplied by regulatory authorities (LGC Standards Ltd., Teddington, UK; DLA—Proficiency Tests GmbH, Sievershütten, Germany; LVU Lippold, Herbolzheim, Germany). Food and pet food products were obtained from official food control agencies and supermarkets. The study mainly focused on sausages and pet food containing game species because these products are known to be vulnerable to the substitution of high-value game ingredients by lower-quality, cheaper meat species.
Reference samples were analyzed in “laboratory 1” (Chemical and Veterinary Analytical Institute Muensterland-Emscher-Lippe (CVUA-MEL) in cooperation with Chemical and Veterinary Analytical Institute Ostwestfalen-Lippe (CVUA-OWL), where sequencing was performed. A subset of seven reference samples was also analyzed in “laboratory 2” (Austrian Agency for Health and Food Safety (AGES)). Commercial food and pet food samples were analyzed independently either in laboratory 1 or laboratory 2.

2.2. DNA Extraction and Quantification

After homogenization and prior to DNA isolation, all samples were lysed in the presence of a lysis buffer and proteinase K solution at elevated temperature under constant shaking. Afterwards, DNA extraction was performed using commercially available kits. DNA from reference samples was isolated with either the Wizard Genomic DNA Purification Kit, the Wizard DNA Clean-Up Kit or the Maxwell 16 FFS Nucleic Acid Extraction Kit from Promega (Madison, WI, USA) according to the respective manufacturer’s instruction sheet. DNA from food and pet food samples was extracted with either the DNeasy mericon Food Kit (Qiagen, Hilden, Germany) or the Maxwell 16 FFS Nucleic Acid Extraction Kit (Promega, Madison, WI, USA), following the instructions of the manufacturers. DNA isolates were stored at −20 °C. Before DNA library preparation, the concentration of individual DNA extracts was determined either with a spectrophotometer (Eppendorf, Hamburg, Germany) or a Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) by using the dsDNA BR assay kit (Thermo Fisher Scientific, Waltham, MA, USA).

2.3. DNA-Library Preparation and NGS

A ~120 base pair fragment of the mitochondrial 16S rDNA gene was used as barcode region for species identification. Library preparation was carried out as described previously [28] with minor modifications. PCR products were indexed using the Illumina Nextera XT Index Kit v2 set A-D or the IDT-Illumina Nextera DNA UD Indexes Kit (Illumina, San Diego, CA, USA). Paired-end sequencing (2 × 150 bp) was performed with either the MiSeq Reagent Kit v2 or the MiSeq Reagent Kit v2 Micro (Illumina, San Diego, CA, USA) at a final loading concentration between 8–10 pM, depending on the instrument and the reagent kit, using the MiSeq system. PhiX DNA, added at a concentration of ~5%, served as sequencing control.

2.4. NGS Data Analysis Using Galaxy

After paired-end sequencing and FastQ file generation via on-board MiSeq Control software (version 2.6.2.1, Illumina, San Diego, CA, USA) and MiSeq Reporter software (version 2.6.2.3, Illumina, San Diego, CA, USA), the resulting FastQ files were used as input for data analysis. Afterwards, the previously uploaded files were processed according to the analysis pipeline as described previously [28] by using the Galaxy platform with the following modifications: the target-specific primer sequences were trimmed off with Cutadapt, Galaxy Version 1.16.6 [29] instead of using the tool Trim (Galaxy Version 0.0.1). Moreover, NGS reads were not clustered into operational taxonomic units (OTUs). After completely identical reads were collapsed into a representative sequence with the tool Dereplicate, Galaxy Version 1.0.0 [30], these sequences were directly matched against a customized database including 51 mitochondrial genomes from animals using BLASTn.

2.5. DNA Array and Real-Time PCR Assays

The LCD Array Kit MEAT 5.0 (Chipron GmbH, Berlin, Germany), allowing the simultaneous detection of 17 mammalian and seven bird species, was performed following the manufacturer’s instruction. Data analysis was done with the SlideReader Software (version 12, 2012-01, Chipron GmbH, Berlin, Germany).
Real-time PCR assays for the detection and quantification of meat species were performed following protocols published previously [11,14,16,18,31,32,33,34,35]. Quantification was carried out either by normalization with DNA extract from material of defined composition or relatively by using a reference real-time PCR assay [13].

3. Results and Discussion

In order to investigate the applicability of the DNA metabarcoding method for routine analysis, a total of 104 samples were analyzed. The samples consisted of 25 reference samples, 56 food products, and 23 pet food products. In addition to DNA metabarcoding, each sample was analyzed by real-time PCR and/or a commercial DNA array to evaluate the reliability of the DNA metabarcoding method. Results obtained by DNA metabarcoding are expressed as the ratio of the number of reads that were assigned to the respective meat species and the total number of reads that passed the amplicon analysis pipeline. The results obtained by the commercial DNA array are given as “positive” or “negative”, results obtained by real-time PCR as a ratio of DNA (%).

3.1. Reference Samples

Twenty-five reference samples were analyzed, comprising eight meat mixtures, four dairy products and thirteen boiled sausages. Reference samples contained from two to 14 meat species in a ratio from 1.0 to 99.0% (w/w) (Table 1). In total, 20 different animal species, including 14 mammalian species (moose, kangaroo, sheep, buffalo, horse, cattle, hare, goat, red deer, pork, rabbit, roe deer, reindeer and fallow deer) and six poultry species (ostrich, pheasant, Muscovy duck, turkey, goose, and chicken) were present in the reference samples. Results obtained by DNA metabarcoding, DNA array and real-time PCR assays are summarized in Table 1.

3.1.1. Qualitative Results

The DNA metabarcoding method allowed the detection of 19 out of the 20 animal species covered by the reference samples. Fallow deer could not be detected because the DNA barcode region of fallow deer is not amplified due to two mismatches in the reverse primer (unpublished data). The DNA metabarcoding method allowed accurate identification of animal species in meat mixtures, dairy products, and boiled sausages. Species could be identified correctly down to a ratio of 1% (w/w). Goat DNA was detected at low concentration (0.3%) in one dairy sample (DLA45-2), although goat was not added intentionally. Notably, for this sample, proficiency test results were inconsistent (some were positive, some negative) [36].
The commercial DNA array and real-time PCR assays also allowed correct identification of all species contained. In contrast to the DNA metabarcoding method, goat was not detected in the dairy sample DLA45-2.
A subset of seven reference samples, including four dairy products (DLA45 1–4) and three boiled sausages (Lippold A–C, 2019), was independently subjected to DNA metabarcoding analysis at the AGES (laboratory 2, Table 1). In spite of small differences in the workflow, including a different sequencing chemistry, the species identified were identical, demonstrating the robustness of the DNA metabarcoding method. In line with laboratory 1, goat DNA was detected in dairy sample DLA45-2.

3.1.2. Quantitative Results

In order to investigate the applicability of the DNA metabarcoding method for obtaining quantitative results, we calculated the relative quantification error (RQE, absolute difference between the expected and experimentally determined ratio of the species contained in the sample, normalized by the expected value). RQE of the DNA metabarcoding method depended on the ratio of the species in the reference sample (Figure 1A). For species being present at a concentration ratio ≤5%, the median of RQE was 33%. For concentration ratios ranging from 5% to 20%, the median RQE was slightly higher (42%). As expected, the lowest RQE (7%) was obtained for concentration ratios >20%.
In Figure 1B, the RQE is shown for each of the 19 species detected by DNA metabarcoding. For eight mammalian (moose, kangaroo, sheep, buffalo, horse, cattle, hare, and goat) and five poultry species (ostrich, pheasant, Muscovy duck, turkey, and goose), the median RQE was <50%. For four mammalian species (red deer, pork, rabbit, and roe deer) and chicken, the median RQE was between 50% and 100%. The highest median RQE was obtained for reindeer (133%).
RQE was also calculated for real-time PCR (difference between the ratio of the species contained in the reference sample (Table 1, column 3) and the ratio of DNA (%) determined by real-time PCR (Table 1, column 5), divided by the ratio of the species contained in the reference sample (Table 1, column 3)). The boxplot in Figure 2A shows the distributions of RQE determined by DNA metabarcoding and real-time PCR. Median and interquartile ranges for NGS and PCR errors are 39.7% (7.8%–59.9%) and 36.9% (11.4%–67.9%), respectively, indicating that the two distributions largely overlap.
For all major components (cattle, sheep; 95% or 99%) in meat mixtures, the RQE of the DNA barcoding method and real-time PCR was <6%. For the minor component (horse, turkey; 1%, 5%) in samples LGC7240, LGC7247, and LGC7246, the RQE of both methods was <30%. Both DNA metabarcoding and real-time PCR led to substantially too high ratios (RQE 94%—200%) for cattle as minor component (LGC7249, 5%; LGC7248, 1%). The content of pork (1%) in sample LGC7242 was substantially overestimated (RQE 80%) by DNA metabarcoding, but not by real-time PCR.
Each of the four dairy products contained one major component (cattle, buffalo, or goat) and one, two, or three minor components (buffalo, cattle, sheep, or goat). The major components could be quantified with the RQE <30% with both methods. Only in sample DLA45-3, cattle was substantially underestimated by real-time PCR (RQE 37%). Due to high lipid content and harsh processing procedures, DNA isolated from dairy products is frequently not amplified efficiently [37]. Underestimation of cow milk compared to goat milk by real-time PCR has already been reported by Rentsch et al. and was explained by the relatively low number of somatic cell counts in cow milk compared to goat milk [31]. In the case of minor components, for buffalo (8%) and cattle (10%) in samples DLA45-1 and DLA45-2, respectively, the RQE of DNA metabarcoding and real-time PCR was ≤24%. Goat (11%) was substantially overestimated in sample DLA45-3 (RQE 214% and 298%), and sheep (10%) substantially underestimated in DLA45-4 (RQE 53% and 66%) by DNA barcoding and real-time PCR.
The number of species in 13 boiled sausages ranged from two (DLA44-1) to 14 (Lippold-C, 2020 and Lippold-A, 2021). For major components at a ratio >85% (pork in samples DLA44-1, DLA44-3, and DLAptAUS2-3.1), the RQE of DNA metabarcoding and real-time PCR was <10%. The major components at a ratio of between 85% and 20% (Lippold-A, 2013: cattle, chicken; Lippold-C, 2019: pork; Lippold-A, 2020: horse; Lippold-B, 2020: pork; Lippold-B, 2021: pork; Lippold-C, 2021: cattle) were underestimated by DNA metabarcoding and real-time PCR, with the RQE ranging from 33% to 67% and 31% to 75%, respectively. A number of minor components at a ratio of between 20% and 5% could be quantified with RQE <30% by either DNA metabarcoding (e.g., Lippold-A, 2013: sheep, Muscovy duck; Lippold-A, 2019; Lippold-C, 2020: sheep), or real-time PCR (e.g., Lippold-A, 2019: red deer; Lippold-B, 2019: chicken, turkey) or both methods (e.g., Lippold-A, 2019: sheep, pheasant; Lippold-B, 2020: chicken).
For cattle in samples Lippold-C, 2019 and Lippold-B, 2021 ratios of 1.1/1.2% (NGS) and 1.8% (PCR) or 2.8% (NGS) and 1.8% (PCR) were determined, respectively. Cattle was not added intentionally to these samples, but was contained as traces probably due to production-related carryover. Results of both proficiency tests showed that most participants (86% and 97%) also identified cattle in these samples.
Quantitative data sets obtained for the subset of seven reference samples analyzed in laboratory 1 and laboratory 2 by DNA metabarcoding showed a very good correlation (r2 = 0.988) (Figure 2B), indicating the high reproducibility of the method. In conclusion, we found that the RQE was quite variable and depended on both the concentration and the identity of the analyte. Additionally, the error was comparable to that of PCR, the current gold-standard method.
Overall our data confirm the limitations known for DNA quantification in meat products [23]. Due to the differences in tissue type, the number of cells per unit of mass, genome size, processing grade and DNA extractability, quantitative results derived from DNA-based methods should serve only as rough estimates for weight ratios of different species in food and feed [8]. During manual and industrial production of meat products production-related carryover of undeclared animal species regularly occurs. In routine analysis of samples in public laboratories, mass concentrations below 1% (w/w) are generally reported as possible process contaminants and do not constitute a violation of declaration. Considering the high quantitation errors of DNA-based methods, in most cases a factor of five might be appropriate to discriminate between production-related carryover of undeclared species and mislabeling.

3.2. Commercial Food Products

Table 2 summarizes the results obtained by DNA metabarcoding, real-time PCR and DNA array for 56 commercial food products obtained from food control agencies or purchased at local supermarkets. The samples comprised 34 sausages, including seven wild boar sausages, 20 deer sausages and seven further sausages, six vertical rotating meat spits, seven pâtés, two minced meat products, one steak, two convenience foods, and four milk products.
Table 2 indicates that DNA metabarcoding and real-time PCR and/or the commercial DNA array led to identical qualitative results for the 56 commercial food products. However, for discrimination of meat from wild boar (Sus scrofa scrofa) and meat from domestic pig (Sus scrofa domesticus), results of two singleplex real-time PCR assays and/or a duplex real-time PCR assay developed recently had to be taken into account [38]. Neither the DNA metabarcoding method nor common real-time PCR assays for pork allow distinguishing between wild boar and pork, yielding only information on the total ratio of wild boar and pork DNA. This is due to the fact that the genomes of the two subspecies are highly homologous and hybridization and back-crossings increased sequence homologies and intra-subspecies variability [39,40].
The ingredient list of 14 out of 20 deer sausages did not contain any information on the deer species (red deer, sika deer, fallow deer). Red deer, roe deer, red deer and roe deer, and red deer and sika deer were detected with DNA ratios >1% in eight, one, three and two of these sausages, respectively. Four and two out of the 20 deer sausages were declared to contain roe deer and red deer, respectively. Our results confirmed the presence of these deer species in the respective food products.
For all species detected in deer sausage 17, sausage 5 and 6, pâté 7 and minced meat product 1 (Table 2), the ratios obtained by DNA metabarcoding and real-time PCR differed by less than 30%. However, in the cases of the other food products, differences >30% were observed for at least one of the species identified.
Comparison of our results, obtained by DNA metabarcoding and real-time PCR and/or the DNA array, with the food ingredient lists revealed multiple discrepancies (Table 2). In a number of commercial food products, species that were not given on the food label were detected by both DNA metabarcoding and real-time PCR and/or the DNA array. Most frequently, the DNA of undeclared species was found in high ratios >5%, indicating that the replacement of meat species by cheaper alternatives is an ongoing food fraud issue. For some products, the species detected were declared but the DNA ratios determined did not correspond with declaration (“declared and detected, ratio suspicious”). In further products, the DNA of undeclared species was detected in traces between 1% and 5%, which were possibly contained due to production-related carry-over. In only one product (wild boar sausage 5), a species declared (chamois) was not detected. Figure 3A summarizes the number of mislabeled species by type of fraud in commercial foodstuffs, Figure 3B the number of mislabeled species by type of food product.

3.3. Commercial Pet Food Products

The applicability of the DNA metabarcoding method was also investigated by analyzing 23 pet food products. The following species were given on the food label: deer, roe deer, cattle, sheep, rabbit, chicken, turkey, duck, Muscovy duck, and ostrich. Table 3 indicates that qualitative results obtained by DNA metabarcoding were in line with those obtained by real-time PCR and/or the commercial DNA array. For some animal species, e.g., red deer in samples 1, 3, 12; pork in sample 2, 10, 19, 21; and chicken in samples 19, 22; the ratios determined by DNA metabarcoding and real-time PCR differed by less than 30%. However, in other cases, differences in the ratios >30% were obtained (Table 3).
Fifteen out of the 23 pet food products were declared to contain deer, without disclosing the deer species. DNA metabarcoding and real-time PCR and/or the commercial DNA array detected red deer in six, red deer and roe deer in four and reindeer in one out of these 15 pet food products. In four pet food products (samples 5, 8, 11, and 21), deer was neither detected by DNA metabarcoding nor by real-time PCR and/or the commercial DNA array. Identical qualitative results were also obtained for three pet food products declared to contain roe deer (samples 12, 16, and 18). Each of the methodologies applied yielded a negative result for roe deer, but a positive result for red deer.
In sample 18, sika deer (16.6%) was detected by DNA metabarcoding. Since sika deer is rarely used in pet food products, sample 18 was not analyzed by a real-time PCR assay for sika deer and the DNA array used does not detect sika deer. This example illustrates one of the main limitations of using PCR for meat species authentication: animal species that are not expected will not be detected [41].
In a high number of commercial pet food products, undeclared species were detected by each of the methodologies applied. Most frequently, undeclared species, were present at a ratio >5%, e.g., pork, chicken, cattle, mallard, and turkey (Figure 4). These animal species of lower commercial value mainly replaced deer, either totally or in part. The results show that inspection of pet food for authenticity has high relevance.
In some products, undeclared species were detected in a ratio between 1% and 5%. Most probably, these species were present due to production-related carry-over. Chicken, roe deer, deer, ostrich and game could not be identified in several pet food products although they were declared to contain these species. In some products, the declared species was detected but the DNA ratio determined drastically differed from the content given on the label (Figure 4). These results are probably caused by total or partial degradation of DNA due to high processing grades of the respective raw materials.

3.4. Cost Analysis

Metabarcoding could be an attractive alternative to real-time PCR in species differentiation, especially due to the possibility of analyzing many samples simultaneously for many species. A detailed cost comparison with the standard real-time PCR method is not yet available. For the present publication a break-even analysis was performed, based on current data from AGES cost accounting, to show what effect the number of samples and the number of parameters (animal species) has on the choice of methodology used. The break-even point or volume (BEP) represents the number of tested samples/parameters where the real-time PCR-based cost equals the NGS-based cost. Above this threshold, an NGS-based approach generates savings. Figure 5A shows the BEP for NGS of a maximum of 21 animal species, corresponding to 21 real-time PCR methods for animal species available in the AGES laboratory. The analysis shows that the use of NGS is more cost-effective for the detection of 21 animal species from the tenth sample onwards. If no multiplex methods for real-time PCR are available in the laboratory, NGS is already profitable from the fifth sample onwards. If the scope of testing is limited to only up to seven animal species per sample, real-time PCR is always cheaper than NGS analysis. Figure 5B shows the BEP at full capacity of the sequencing kit. If the sequencing kit is fully utilized (Illumina MiSeq v2 chemistry, 75 samples, 200,000 reads per sample), the costs per sample are significantly reduced. In this case, NGS is already cheaper from the first sample onwards, if at least 15 parameters are analyzed. Below a parameter number of seven, however, real-time PCR always remains the cheaper method.

4. Conclusions

By analyzing 25 reference samples, 56 commercial food and 23 pet food products using DNA metabarcoding and real-time PCR and/or a commercial DNA array, we demonstrated that the DNA metabarcoding method developed recently is a suitable screening method for meat species authentication. Qualitative and quantitative results of the DNA metabarcoding method were in line with those obtained by real-time PCR. The results from independent analyses in two laboratories indicate the robustness and reproducibility of the DNA metabarcoding method. Our data on reference samples confirm the limitations known for DNA quantification in meat products. Quantitative results derived from DNA-based methods should serve only as rough estimates for weight ratios of different species in food and feed.
A major advantage of metabarcoding is the parallel detection of a large number of animal species including species not tested routinely or for which no real-time PCR methods are available. Our results indicate that in addition to food products, DNA metabarcoding is particularly applicable to pet food products, which frequently contain multiple animal species and were shown to be also highly prone to adulteration.
For a large number of samples or parameters, metabarcoding is the more cost-effective analysis. By combining different applications (joint sequencing of plant and animal species, bacteria, etc.), an additional cost reduction is possible, as the sequencing kits, the biggest cost driver, can be better utilized.

Author Contributions

Conceptualization, C.B.-N. and R.H.; methodology, S.D. and R.H.; validation, C.B.-N., S.D., R.H. and H.P.; investigation, S.D., H.P. and L.P.; resources, C.B.-N., R.H. and H.P.; writing—original draft preparation, C.B.-N., M.C.-M. and S.D.; writing—review and editing, C.B.-N., M.C.-M., G.D., S.D., R.H., H.P. and L.P.; visualization, G.D.; supervision, C.B.-N. and R.H.; project administration, C.B.-N.; R.H. funding acquisition, C.B.-N. All authors have read and agreed to the published version of the manuscript.

Funding

We gratefully acknowledge funding for metabarcoding analyses of CVUA-MEL by the Ministry for Environment, Agriculture, Conservation and Consumer Protection of the State of North Rhine-Westphalia.

Institutional Review Board Statement

Not applicable, exclude statement. The study did not involve living animals.

Informed Consent Statement

Not applicable, exclude statement.

Data Availability Statement

The data presented in this study are available on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Foods 10 02875 g001 550
Figure 1. Relative quantification error (RQE) of the DNA-metabarcoding method on reference samples. RQE was calculated as the difference between the expected concentration ratio of a species and the proportion of reads assigned to that species, normalized by the expected concentration ratio. (A) RQE for different concentration ratio ranges. Small points represent a single measurement, large points and lines represent the median and inter-quantile range, respectively. Red: expected concentration <5%, green: expected concentration between 5% and 20%, blue: expected concentration >20%. (B) RQE by species. RQE calculated as for (A) is represented for each species, the number of data points (including those obtained in laboratory 2 (AGES)) is indicated in parenthesis. Species are sorted according to their median RQE from top (lowest) to bottom (highest). Small points represent a single measurement, large points and lines represent the median and inter-quantile range, respectively.
Figure 1. Relative quantification error (RQE) of the DNA-metabarcoding method on reference samples. RQE was calculated as the difference between the expected concentration ratio of a species and the proportion of reads assigned to that species, normalized by the expected concentration ratio. (A) RQE for different concentration ratio ranges. Small points represent a single measurement, large points and lines represent the median and inter-quantile range, respectively. Red: expected concentration <5%, green: expected concentration between 5% and 20%, blue: expected concentration >20%. (B) RQE by species. RQE calculated as for (A) is represented for each species, the number of data points (including those obtained in laboratory 2 (AGES)) is indicated in parenthesis. Species are sorted according to their median RQE from top (lowest) to bottom (highest). Small points represent a single measurement, large points and lines represent the median and inter-quantile range, respectively.
Foods 10 02875 g001
Foods 10 02875 g002 550
Figure 2. Precision and reproducibility of the DNA-metabarcoding method. (A) Comparison of RQE of the DNA metabarcoding method (red) compared to that of real-time PCR (blue). Only species for which quantitative PCR was performed are represented. Black points represent single measurement and grey lines connect paired values. Colored boxes represent the interquartile range with the horizontal line at the median and whiskers represent the Tukey-corrected minimum and maximum. Although a significant difference between the two distributions was calculated (paired Wilcoxon rank test p = 0.048), the quantitative difference is too small to be biologically relevant. (B) Reproducibility of DNA metabarcoding quantification in two different laboratories. A subset of the samples was quantified with the DNA metabarcoding method in laboratory 1 (CVUA-MEL, x-axis) and laboratory 2 (AGES, y-axis), with highly similar results. A linear regression (blue) of both datasets showed a slope of 1 and a Pearson correlation coefficient r² = 0.988. Each point represents a single observation.
Figure 2. Precision and reproducibility of the DNA-metabarcoding method. (A) Comparison of RQE of the DNA metabarcoding method (red) compared to that of real-time PCR (blue). Only species for which quantitative PCR was performed are represented. Black points represent single measurement and grey lines connect paired values. Colored boxes represent the interquartile range with the horizontal line at the median and whiskers represent the Tukey-corrected minimum and maximum. Although a significant difference between the two distributions was calculated (paired Wilcoxon rank test p = 0.048), the quantitative difference is too small to be biologically relevant. (B) Reproducibility of DNA metabarcoding quantification in two different laboratories. A subset of the samples was quantified with the DNA metabarcoding method in laboratory 1 (CVUA-MEL, x-axis) and laboratory 2 (AGES, y-axis), with highly similar results. A linear regression (blue) of both datasets showed a slope of 1 and a Pearson correlation coefficient r² = 0.988. Each point represents a single observation.
Foods 10 02875 g002
Foods 10 02875 g003 550
Figure 3. Wrong declarations in foodstuffs (A) Breakdown of wrongly labeled species by type of fraud in foodstuffs. Each box represents a single species, the size of the box indicates the number of times that this species appeared for each type of fraud in the dataset. (B) Breakdown of wrongly labelled species by type of food product. Each box represents a single species, the size of the box indicates the number of times that this species appeared for each type of food product in the dataset.
Figure 3. Wrong declarations in foodstuffs (A) Breakdown of wrongly labeled species by type of fraud in foodstuffs. Each box represents a single species, the size of the box indicates the number of times that this species appeared for each type of fraud in the dataset. (B) Breakdown of wrongly labelled species by type of food product. Each box represents a single species, the size of the box indicates the number of times that this species appeared for each type of food product in the dataset.
Foods 10 02875 g003
Foods 10 02875 g004 550
Figure 4. Breakdown of wrongly labelled species by type of fraud in pet food products. Each box represents a single species, the size of the box indicated the number of times that this species appeared for each type of fraud in the dataset.
Figure 4. Breakdown of wrongly labelled species by type of fraud in pet food products. Each box represents a single species, the size of the box indicated the number of times that this species appeared for each type of fraud in the dataset.
Foods 10 02875 g004
Foods 10 02875 g005 550
Figure 5. Break-even point analysis of NGS-metabarcoding and real-time PCR for the qualitative identification of bird and mammal species. The left-most orange area corresponds to combinations of sample number/parameter number for which PCR is always cheaper than NGS. The right-most green area corresponds to combinations for which NGS is always cheaper than PCR. The middle blue zone corresponds to combinations for which the cost difference largely depends on the degree of multiplexing of the PCRs. NGS costs were calculated for two exemplary laboratories: (A) a laboratory running exclusively meat-metabarcoding runs, and (B) a laboratory running full-capacity sequencing runs, for example, mixing samples with other type of assays.
Figure 5. Break-even point analysis of NGS-metabarcoding and real-time PCR for the qualitative identification of bird and mammal species. The left-most orange area corresponds to combinations of sample number/parameter number for which PCR is always cheaper than NGS. The right-most green area corresponds to combinations for which NGS is always cheaper than PCR. The middle blue zone corresponds to combinations for which the cost difference largely depends on the degree of multiplexing of the PCRs. NGS costs were calculated for two exemplary laboratories: (A) a laboratory running exclusively meat-metabarcoding runs, and (B) a laboratory running full-capacity sequencing runs, for example, mixing samples with other type of assays.
Foods 10 02875 g005
Table
Table 1. Results obtained for reference samples. DNA array and real-time PCR results were obtained in laboratory 1. DNA metabarcoding results were obtained in laboratory 1, except those marked by footnote 5.
Table 1. Results obtained for reference samples. DNA array and real-time PCR results were obtained in laboratory 1. DNA metabarcoding results were obtained in laboratory 1, except those marked by footnote 5.
Reference SampleCompositionResults
SpeciesRatio (%, w/w)DNA Metabarcoding Ratio of Reads (%) 4Real-Time PCR (Ratio of DNA (%)) or DNA Array (Positive/Negative)
LGC7242cattle99.098.298.9 1
pork1.01.81.1 1
LGC7240cattle99.098.895.9 1
horse1.01.21.3 (Equidae) 1
LGC7249sheep95.090.190.3 1
cattle5.09.99.7 1
LGC7248sheep99.097.797.0 1
cattle1.02.33.0 1
LGC7245sheep95.098.493.0 1
chicken5.01.67.0 1
LGC7244sheep99.0100.099.9 1
chicken1.0<0.10.1 1
LGC7247sheep95.096.394.9 1
turkey5.03.85.1 1
LGC7246sheep99.098.898.8 1
turkey1.01.21.2 1
DLA44-1, 2019pork93.489.688.5 1
horse6.610.411.5 (Equidae) 1
DLA44-3, 2019pork87.387.485.1 1
turkey7.07.611.3 1
cattle5.65.13.6 1
DLA45-1, 2019cattle92.091.8/94.2 590.7 1
buffalo8.08.0/5.7 59.3 1
DLA45-2, 2019buffalo81.072.5/72.3 571.5 1
cattle10.010.5/11.6 57.6 1
sheep9.016.7/15.7 520.9 1
goatnot added 60.3/0.3 5negative 3
DLA45-3, 2019cattle89.065.5 / 73.0 556.2 1
goat11.034.5/27.0 543.8 1
DLA45-4, 2019goat90.095.2/94.2 596.9 1
sheep10.04.7/5.6 53.4 1
DLAptAUS2-3.1, 2020pork90.998.799.7 1
donkey9.11.1positive 3
horsenot added 60.20.3 (Equidae) 1
Lippold-A, 2013cattle27.818.514.7 2
sheep16.714.06.6 2
chicken22.210.815.3 2
goose11.115.7positive 3
Muscovy duck11.112.8positive 3
roe deer11.128.218.1 2
Lippold-A, 2019red deer16.022.8/24.4 513.2 2
cattle15.69.1/11.2 522.2 2
ostrich15.317.6/19.9 5positive 3
hare14.48.6/7.6 5positive 3
kangaroo14.216.8/9.1 5positive 3
sheep12.612.5/13.9 510.3 2
pheasant12.012.5/14.0 510.5 2
Lippold-B, 2019goose16.423.2/23.0 5positive 3
rabbit15.53.7/2.6 5positive 3
chicken14.97.6/6.8 516.6 2
pork13.621.4/21.7 52.9 2
moose13.613.0/13.3 5positive 3
roe deer13.524.5/26.4 523.8 2
turkey12.46.6/6.2 58.7 2
Lippold-C, 2019pork28.99.6/8.8 58.2 2
horse17.819.4/17.2 510.6 (Equidae) 2
Muscovy duck16.419.9/22.5 5positive 3
reindeer13.832.0/32.4 5positive 3
goat12.06.7/6.8 52.8 2
fallow deer11.1-12.6 2
cattletraces 71.1/1.2 51.8 2
Lippold-A, 2020goose38.849.9positive 3
horse25.028.512.9 (Equidae) 2
pork12.53.79.1 2
hare11.26.8positive 3
Muscovy duck10.09.6positive 3
turkey2.51.52.3 2
Lippold-B, 2020pork31.312.210.2 2
fallow deer24.1-12.9 2
reindeer17.945.0positive 3
chicken12.59.415.9 2
goat11.77.53.7 2
turkey2.41.81.6 2
Lippold-C, 2020goose8.114.5positive 3
red deer8.110.510.8 2
cattle7.93.921.2 2
rabbit7.74.0positive 3
chicken7.44.213.0 2
hare7.32.2positive 3
kangaroo7.26.5positive 3
pork6.711.32.5 2
moose6.77.1positive 3
roe deer6.714.422.4 2
sheep6.35.22.8 2
turkey6.13.55.4 2
pheasant6.05.0positive 3
ostrich7.77.7positive 3
Lippold-A, 2021cattle8.58.04.1 2
pork6.310.63.1 2
sheep7.84.76.2 2
horse6.33.83.5 (Equidae) 2
red deer7.814.17.4 2
fallow deer6.3-3.8 2
roe deer6.311.611.3 2
moose6.36.4positive 3
kangaroo7.48.1positive 3
rabbit7.11.7positive 3
reindeer6.19.8positive 3
chicken9.84.612.2 2
turkey6.32.65.7 2
ostrich7.87.8positive 3
Lippold-B, 2021cattletraces 72.81.8 2
pork32.610.614.2 2
horse4.34.02.0 (Equidae) 2
roe deer14.427.427.4 2
moose10.919.7positive 3
kangaroo13.912.7positive 3
hare10.98.4positive 3
pheasant13.114.4positive 3
Lippold-C, 2021cattle25.014.96.2 2
pork13.914.52.3 2
sheep14.312.93.9 2
goat16.47.32.2 2
red deer12.120.26.6 2
goose7.815.9positive 3
Muscovy duck10.414.5positive 3
-: Not detected. 1 Relative quantification based on normalization. 2 Relative quantification by using a reference real-time PCR assay. 3 Obtained by the DNA array. 4 For samples containing fallow deer, ratios of reads refer to 100% minus ratio (%, w/w) of fallow deer. 5 Obtained in laboratory 2 (AGES). 6 Proficiency test results were inconsistent, some were positive, some negative. 7 Species not added intentionally, but identified by 86% (Lippold-C, 2019) and 97% (Lippold-B, 2021) of the participants of the proficiency test.
Table
Table 2. Results for commercial food products.
Table 2. Results for commercial food products.
Result
SampleAnimal Species DeclaredAnimal Species DetectedDNA Metabarcoding Ratio of Reads (%)Real-Time PCR (Ratio of DNA (%)) or DNA Array (Positive/Negative)Comment
wild boar sausage 1wild boar, pork, pork baconpork83.0 452.5 1
wild boar35.2 1
red deer15.13.9 1not declared, >5%
cattle1.78.4 1not declared, 1%–5%
wild boar sausage 2wild boar, pork, pork baconwild boar86.9 423.7 1
pork64.1 1
red deer13.112.3 1not declared, >5%
wild boar sausage 355% wild boar, 36% roe deerroe deer60.740.8 1
pork25.1 450.5 1
wild boar<1.0 1declared and detected 3
cattle14.08.7 1not declared, >5%
wild boar sausage 474% red deer,
22% wild boar bacon		cattle30.246.4 1not declared, >5%
pork28.8 443.5 1not declared, >5%
wild boar<1.0 1declared and detected, r.s.
red deer22.810.1 1declared and detected, r.s.
chamois18.2-not declared, >5%
wild boar sausage 5chamois, wild boar, roe deer, pork baconpork48.8 48.9 1
wild boar38.2 1
red deer36.842.0 1not declared, >5%
roe deer14.010.9 1
chamois0.0-declared, but not detected
wild boar sausage 6no declarationpork62.2 428.5 1
wild boar26.1 1
roe deer24.416.0 1
cattle13.429.4 1
wild boar sausage 7game, cattle, pork baconpork70.2 460.3 1
wild boar16.0 1
cattle28.723.7 1
roe deer<1.0<1.0 1
sheep<1.0<1.0 1
deer sausage 1deer, pork, pork baconred deer72.052.6 1
pork19.541.6 1
cattle8.55.8 1not declared, >5%
deer sausage 2roe deer, pork, pork baconroe deer52.028.3 1
pork22.8 454.3 1
wild boar<1.0
cattle10.87.9 1not declared, >5%
red deer14.39.5 1not declared, >5%
deer sausage 3roe deer, porkroe deer89.975.1 1
pork5.923.0 1
cattle4.21.9 1not declared, 1%–5%
deer sausage 4deer, porkred deer67.052.3 1
pork33.0 447.7 1
wild boar<1.0 1
deer sausage 5roe deer, pork, pork baconroe deer81.578.5 1
pork9.315.8 1
cattle9.05.7 1not declared, >5%
red deer< 1.0<1.0 1
deer sausage 6game, porkred deer83.866.9 1
cattle9.314.9 1not declared, >5%
pork5.2 415.3 1
wild boar<1.0
roe deer1.73.0 1
deer sausage 770% red deer, 30% porkred deer70.472.2 1
pork29.6 4<1.0 1
wild boar27.8 1
deer sausage 8deer, pork, pork baconred deer68.046.7 1
pork31.753.3 1
sika deer<1.0-
deer sausage 9deer, pork, pork baconroe deer79.058.7 1
pork20.941.3 1
deer sausage 10deer, pork, pork baconred deer74.138.5 1
pork25.361.5 1
deer sausage 11deer, pork, pork baconred deer72.036.4 1
pork27.863.6 1
deer sausage 12pork, red deerpork66.651.9 2
red deer33.448.1 2
deer sausage 13roe deer, pork, pork baconroe deer81.667.4 1
pork18.3 432.6 1
wild boar<1.0 1
deer sausage 14deer, pork, pork bacon, cattle casing 5red deer70.648.7 1
pork25.750.0 1
sika deer2.61.3 1
roe deer<1.0<1.0 1
deer sausage 15pork, deer, cattlered deer51.439.9 1
pork31.145.3 1
cattle16.914.8 1
roe deer< 1.0<1.0 1
deer sausage 16deer, pork, pork bacon, cattle casing 5red deer53.627.2 1
pork24.261.3 1
sheep21.611.4 1not declared, >5%
roe deer<1.0<1.0 1
fallow deer-<1.0 1
deer sausage 17deer, pork, cattleroe deer55.859.0 1
red deer24.524.6 1
pork10.2 48.9 1
wild boar<1.0 1
cattle9.47.5 1
deer sausage 18deer, cattlered deer66.153.8 1
cattle32.246.2 1
sika deer1.3<1.0 1
deer sausage 19deer, cattlered deer76.943.9 1
cattle20.156.1 1
sika deer2.9<1.0 1
deer sausage 20game, cattle, pork baconred deer78.380.9 1
roe deer14.57.0 1
pork5.410.4 1
cattle1.81.7 1
sausage 1chamois, cattle, pork baconred deer35.811.9 1not declared, >5%
pork29.270.0 1declared and detected, r.s.
cattle19.88.9 1
roe deer13.49.2 1not declared, >5%
chamois1.6-declared and detected, r.s.
sausage 260% sheep, 35% pork, 5% goatsheep44.535.7 1
pork27.049.7 1
red deer12.53.9 1not declared, >5%
cattle8.59.4 1not declared, >5%
goat7.41.4 1
sausage 3cattlewater buffalo67.0-not declared, >5%
cattle33.022.9 2
sausage 442% cattle, 35% chickenchicken86.096.4 1
cattle13.53.6 1declared and detected, r.s.
sausage 540% poultry, 15% cattle, cattle fatturkey44.436.4 1
chicken30.132.9 1
cattle25.030.7 1
sausage 6pork, cattle or lambcattle53.659.5 1
pork46.140.5 1
sausage 7lamb, porksheep80.171.7 1
pork19.828.3 1
vertical rotating meat spit 195% beefcattle64.985.4 1
turkey35.114.6 1not declared, >5%
vertical rotating meat spit 275% veal, 20% turkeycattle57.576.0 1
turkey35.421.1 1
chicken7.12.9 1not declared, >5%
vertical rotating meat spit 370% veal, 20% turkeycattle59.274.1 1
turkey33.721.5 1
chicken7.24.4 1not declared, >5%
vertical rotating meat spit 4turkeyturkey98.294.8 1
cattle1.75.2 1not declared, 1%–5%
vertical rotating meat spit 555% cattle, 10% turkey, 25% chickencattle58.829.1 1
chicken23.035.0 1
turkey18.135.9 1
vertical rotating meat spit 655% cattle, 35% poultrycattle56.041.9 1
chicken43.558.1 1
turkey< 1.0<1.0 1
pâté 1wild boar, porkpork99.8 4100.0 1
wild boar<1.0 1declared and detected, r.s.
pâté 2game, porkpork57.677.5 1
red deer42.122.5 1
pâté 349% pork, lamb liverpork66.671.9 1
sheep33.428.1 1
pâté 4pork neck and liver, rabbit meatpork96.246.5 2
rabbit3.8positive 3
pâté 5duck meat and breast, poultry liverturkey49.121.3 2
mallard28.1positive 3
Muscovy duck22.8positive 3
pâté 650% pork meat,
20% red deer meat		pork57.984.4 2
red deer42.015.6 2
pâté 733% pork meat,
20% roe deer meat		roe deer59.759.9 2
pork40.340.1 2
minced meat product 1chicken, cattlechicken76.281.9 2
cattle23.018.1 2
buffalo, kangaroo, fish<1.0positive 3
minced meat product 2lamb, cattlecattle70.351.6 1
sheep29.448.4 1
steakreindeerreindeer100.0positive 3
convenience food 137% pork and cattle, cattle souppork67.982.0 1
cattle32.118.0 1
convenience food 225% pork, cattle souppork87.293.9 1
cattle12.56.1 1
milk product 1goatgoat97.4positive 3
cattle2.6positive 3not declared, 1%–5%
milk product 2goat milkgoat62.8positive 3
sheep36.2positive 3not declared, >5%
ibex<1.0-
cattle<1.0positive 3
milk product 3goat milkgoat62.9positive 3
sheep36.0positive 3not declared, >5%
ibex<1.0-
cattle<1.0negative 3
milk product 4sheep milksheep95.4positive 3
goat4.5positive 3not declared, 1%–5%
-: Not detected, r.s.: ratio suspicious. 1 Relative quantification based on normalization. 2 Relative quantification by using a reference real-time PCR assay. 3 Obtained by the DNA array. 4 Sum of pork and wild boar. 5 In most cases species of casings are not detectable by DNA-based methods.
Table
Table 3. Results for pet food products.
Table 3. Results for pet food products.
Result
SampleAnimal Species DeclaredAnimal Species DetectedDNA Metabarcoding Ratio of Reads (%)Real-Time PCR (Ratio of DNA (%)) or DNA Array (Positive/Negative)Comment
165% deer (heart, liver, lung, rumen)red deer96.392.9 1
pork1.7<1.0 1not declared, 1%–5%
fallow deer-6.0 1
sheep<1.0<1.0 1
chicken<1.0<1.0 1
cattle<1.0<1.0 1
kangaroo<1.0positive 2
260% deer meatpork47.132.6 1not declared, >5%
roe deer36.055.0 1
red deer16.912.4 1
351% deer meat, <2.5% chicken liverred deer96.295.9 1
roe deer2.43.1 1
pork1.0<1.0 1not declared, 1%–5%
rabbit<1.0positive 2
chickennegativenegative 1declared, but not detected
459% fresh meat from deer and roe deer, 1.2% eggshell powderred deer62.453.0 1
mallard29.8positive 2not declared, >5%
chicken6.516.9 1
fallow deer-2.3 1
roe deer<1.0<1.0 1declared and detected, r.s.
pork, sheep, cattle<1.0<1.0 1
510% deer meat
(dried and ground)		chicken38.125.7 1not declared, >5%
turkey12.37.3 1not declared, >5%
mallard10.7positive 2not declared, >5%
horse33.015.8 (Equidae) 1not declared, >5%
Muscovy duck4.6positive 2not declared, 1%–5%
donkey1.1positive 2not declared, 1%–5%
cattle<1.0<1.0 1
deernegativenegative 1, 2declared, but not detected
628% fresh and 26% dried deer meat, 9% chicken fat, 2% dried eggs, 2% fresh and 2% dried herrings, 1% fish oilpork92.239.4 1not declared, >5%
fish-positive 2
chicken3.936.0 1
red deer2.72.3 1declared and detected, r.s.
turkey<1.022.0 1
sheep<1.0<1.0 1
718% dried Muscovy duck meat, 9.4% dried and ground deer meat, 6.3% dried whiting, 6.3% ground wild bones, egg yolk powdercattle67.859.9 1not declared, >5%
chicken9.713.3 1not declared, >5%
mallard7.1positive 2not declared, >5%
red deer7.52.8 1
turkey5.02.3 1not declared, >5%
Muscovy duck1.8positive 2declared and detected, r.s.
sheep<1.0<1.0 1
sika deer<1.0-
goat<1.0positive 2
fish-positive2
850% meat and animal byproducts, 4% ostrich and deerpork52.43.8 1
cattle30.569.6 1
chicken16.826.5 1
turkey<1.0<1.0 1
mallard<1.0negative 2
ostrich, deernegativenegative 2declared, but not detected
935% cattle,
31% poultry,
4% deer		cattle71.443.6 1
turkey9.08.3 1
reindeer12.3positive 2
chicken6.06.4 1
mallard<1.0positive 2
pork, sheep, horse<1.0<1.0 1
red deernegativenegative 1
10lung, meat, kidney, liver, udder, 5% deerpork89.388.8 1
cattle9.710.5 1
red deer<1.0<1.0 1declared and detected, r.s.
chicken<1.0<1.0 1
1148% fresh deer meat, 4% entrails of deermallard96.331.0 1not declared, >5%
goat1.7<1.0 1not declared, 1%–5%
turkey, chicken<1.0<1.0 1
pork, sheep<1.0<1.0 1
deernegativenegative 1declared, but not detected
1250% roe deer (60% meat, 25% heart, 10% lung, 5% liver)red deer98.197.9 1not declared, >5%
horse1.6<1.0 (Equidae) 1not declared, 1%–5%
cattle<1.01.7 1
fallow deer-<1.0 1
roe deernegativenegative 1declared, but not detected
1399% deer meatchicken71.451.8 1not declared, >5%
kangaroo17.6positive 2not declared, >5%
red deer10.33.8 1declared and detected, r.s.
rabbit<1.0positive 2
pork, cattle<1.0<1.0 1
1475% deer (meat, heart, lung)pork84.345.1 1not declared, >5%
cattle6.410.0 1not declared, >5%
roe deer4.238.9 1
mallard2.21.5 1not declared, 1%–5%
turkey1.4<1.0 1not declared, 1%–5%
red deer1.51.7 1
15100% deer meatroe deer65.812.6 1
cattle31.287.0 1not declared, >5%
chicken<1.0<1.0 1
pork1.9<1.0 1not declared, 1%–5%
red deer1.0<1.0 1
1650% roe deerturkey98.299.6 1not declared, >5%
red deer, horse<1.0<1.0 1
pork<1.0<1.0 1
roe deernegativenegative 1declared, but not detected
1760% deerred deer40.425.3 1
cattle36.373.0 1not declared, >5%
pork22.91.7 1not declared, >5%
chicken<1.0<0.1 1
1846% poultry meat, 8% roe deerchicken55.783.8 1
turkey26.713.0 1
sika deer16.6-not declared, >5%
cattle<1.0<1.0 1
red deer, pork<1.0<1.0 1
fallow deer-3.0 1
roe deernegativenegative 1declared, but not detected
1951% meat and animal byproducts, 12% chicken, turkey, duckpork58.555.0 1
chicken26.128.4 1
turkey9.010.1 1
cattle5.34.6 1
mallard<1.0<1.0 1
guinea fowl<1.0<1.0 1
2051% meat and animal byproducts, 12% cattle, sheep, chickenchicken45.253.6 1
pork40.215.3 1
cattle10.926.8 1
sheep3.54.3 1
turkey<1.0positive 2
2133% meat and animal byproducts, 4% poultry, 4% deerpork94.483.6 1
chicken3.915.3 1
guinea fowl<1.0<1.0 1
turkey<1.01.0 1
deernegativenegative 2declared, but not detected
22meat and animal byproducts (4% turkey, 4% duck, 4% game)chicken49.457.6 1
pork25.113.4 1
cattle12.67.3 1
turkey6.319.5 1
duck4.1<1.0 1
sheep1.9<1.0 1
horse<1.0<1.0 (Equidae) 1
fish-positive 2
gamenegativenegative 2declared, but not detected
2340% chicken (heart, meat, liver, stomachs, necks), 28.7% broth, 28% rabbitchicken99.1positive 2
cattle<1.0positive 2
rabbit<1.0positive 2declared and detected, r.s.
-: Not detected, r.s.: ratio suspicious. 1 Relative quantification by using a reference real-time PCR assay. 2 Obtained with the DNA array.
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Preckel, L.; Brünen-Nieweler, C.; Denay, G.; Petersen, H.; Cichna-Markl, M.; Dobrovolny, S.; Hochegger, R. Identification of Mammalian and Poultry Species in Food and Pet Food Samples Using 16S rDNA Metabarcoding. Foods 2021, 10, 2875. https://doi.org/10.3390/foods10112875

AMA Style

Preckel L, Brünen-Nieweler C, Denay G, Petersen H, Cichna-Markl M, Dobrovolny S, Hochegger R. Identification of Mammalian and Poultry Species in Food and Pet Food Samples Using 16S rDNA Metabarcoding. Foods. 2021; 10(11):2875. https://doi.org/10.3390/foods10112875

Chicago/Turabian Style

Preckel, Laura, Claudia Brünen-Nieweler, Grégoire Denay, Henning Petersen, Margit Cichna-Markl, Stefanie Dobrovolny, and Rupert Hochegger. 2021. "Identification of Mammalian and Poultry Species in Food and Pet Food Samples Using 16S rDNA Metabarcoding" Foods 10, no. 11: 2875. https://doi.org/10.3390/foods10112875

APA Style

Preckel, L., Brünen-Nieweler, C., Denay, G., Petersen, H., Cichna-Markl, M., Dobrovolny, S., & Hochegger, R. (2021). Identification of Mammalian and Poultry Species in Food and Pet Food Samples Using 16S rDNA Metabarcoding. Foods, 10(11), 2875. https://doi.org/10.3390/foods10112875

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