The highest number of proteins that interacted strongly with each other were impli-
cated in glycolysis pathway (ALDOB, GAPDH, PKM, and ENO2). Four proteins with close
range were involved in energy metabolic process or structural protein (AK1, CKM, CA3,
and ACTA1). Furthermore, ALB and HSPB11 were exhibited further distance with the
other proteins in the network.

The nodes are differentially abundant proteins (colored nodes—query proteins and first shell of interactors; white nodes—second shell of interactors; empty nodes—proteins of unknown 3D structure; filled nodes—some 3D structure is known or predicted) in Ovis aries database and connection lines are the predicted functional annotations (known interactions: blue—from curated databases; purple—experimentally determined; and predicted interactions: green—gene neighborhood; red—gene fusions; dark blue—gene co-occurrence; and others: yellow—text mining; black—co-expression; light blue—protein homology).

**4. Conclusions**

In the present research, the post-mortem proteome profiles associated with the typical
color-stable (*M. longissimus lumborum*) and color-labile (*M. psoasmajor*) muscles of *Small-
tailed Han* Sheep were studied and compared. The results of differential proteome indicated
that the identified proteins were glycolytic enzymes, energy metabolism enzymes, chaper-
one proteins, structural proteins, and chaperone and binding proteins. Several proteins
were demonstrated to be related to post-mortem discoloration through the methods of
Pearson's correlation and multiple linear regression model analysis between differentially
abundant proteins and meat color indices.

Bioinformatics analysis showed that these proteins (pyruvate kinase, fructose-
bisphosphate aldolase, enolase 2, and glyceraldehyde-3-phosphate dehydrogenase) mainly
involved in the glycolysis pathway. The relationship between post-mortem proteome
profiles and discoloration could play a key role in explaining the different color stabilities
within muscles. However, further research is needed to confirm if the specific differentially
abundant proteins have the potential probability of being predictors for meat discoloration
during post-mortem storage.

**Author Contributions:** Conceptualization, R.D.; Data curation, D.Z.; Formal analysis, X.G.; Funding acquisition, X.G. and R.D.; Investigation, L.W.; Methodology, X.G.; Resources, Y.C.; Software, M.L. and F.Z.; Supervision, R.D.; Validation, S.W.; Writing—review & editing, X.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the financial support by Science and Technology research project of Hebei Education Department (Project No.: BJ2018061); and the Ministry of Agriculture of China (China Agriculture Research System-39, CARS-39).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of China Agricultural University Laboratory Animal Welfare and Animal Experimental Ethical Inspection Form (protocol code: AW30211202-4-1, 30 March 2020).

**Acknowledgments:** This work was supported by the financial support by Science and Technology research project of Hebei Education Department (Project No.: BJ2018061); and the Ministry of Agriculture of China (China Agriculture Research System-39, CARS-39).

**Conflicts of Interest:** The authors declare that there are no conflict of interest.

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Communication

# Preliminary Results about Lamb Meat Tenderness Based on the Study of Novel Isoforms and Alternative Splicing Regulation Pathways Using Iso-seq, RNA-seq and CTCF ChIP-seq Data

Zehu Yuan¹, Ling Ge², Weibo Zhang², Xiaoyang Lv¹, Shanhe Wang², Xiukai Cao¹ and Wei Sun¹,²,*

¹ Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education, Yangzhou University, Yangzhou 225000, China; yuanzehu@yzu.edu.cn (Z.Y.); dx120170085@yzu.edu.cn (X.L.); cxkai0909@163.com (X.C.)

² College of Animal Science and Technology, Yangzhou University, Yangzhou 225000, China; mx120200799@stu.yzu.edu.cn (L.G.); mz120191042@stu.yzu.edu.cn (W.Z.); 007121@yzu.edu.cn (S.W.)

* Correspondence: sunwei@yzu.edu.cn; Tel.: +86-0514-87926332

**Abstract:** Tenderness is an important indicator of meat quality. Novel isoforms associated with meat tenderness and the role of the CCCTC-binding factor (CTCF) in regulating alternative splicing to produce isoforms in sheep are largely unknown. The current project studied six sheep from two crossbred populations (Dorper × Hu × Hu, DHH and Dorper × Dorper × Hu, DDH) with divergent meat tenderness. Pooled Iso-seq data were used to annotate the sheep genomes. Then, the updated genome annotation and six RNA-seq data were combined to identify differentially expressed isoforms (DEIs) in muscles between DHH and DDH. These data were also combined with peaks detected from CTCF ChIP-seq data to investigate the regulatory role of CTCF for the alternative splicing. As a result, a total of 624 DEIs were identified between DDH and DHH. For example, isoform 7.524.18 transcribed from *CAPN3* may be associated with meat tenderness. In addition, a total of 86 genes were overlapped between genes with transcribed DEIs and genes in differential peaks identified by CTCF ChIP-seq. Among these overlapped genes, *ANKRD23* produces different isoforms which may be regulated by CTCF via methylation. As preliminary research, our results identified novel isoforms associated with meat tenderness and revealed the possible regulating mechanisms of alternative splicing to produce isoforms.

**Keywords:** sheep; tenderness; novel isoforms; alternative splicing; CTCF; Iso-seq; ChIP-seq

## 1. Introduction

Meat tenderness is determined by several intrinsic factors (e.g., proteolytic activity, amount of glycogen, fiber type and connective tissue) and extrinsic factors (e.g., animal breed and nutritional conditions) [1]. Among these factors, the calpain system plays an essential role in meat tenderness [2]. In beef, the most important factor that determines tenderness is the proteolytic activity of the calpain system. Calpain3 (CAPN3), a member of the calpain system, is mainly expressed in skeletal muscle, especially in type II fibers [3–5], and it may play an important role in the calpain system to regulate meat tenderness [6]. A previous study suggested that the expression level of CAPN3 protein and mRNA was significantly associated with tenderness [7,8]. Another member of the calpain system is calpastatin (CAST). Accumulated evidence suggests that different isoforms transcribed from a gene due to alternative splicing may play different biological roles. For example, distinct isoforms due to alternative splicing [9] in CAST have different biological functions in beef meat tenderness.

PacBio long-reads isoform sequencing (Iso-seq), a third-generation sequencing technology, can directly obtain full-length isoforms [10]. By combing with traditional RNA-sequencing (RNA-seq), Iso-seq can contribute to identify novel isoform associated with Iso-seq.

**Citation:** Yuan, Z.; Ge, L.; Zhang, W.; Lv, X.; Wang, S.; Cao, X.; Sun, W. Preliminary Results about Lamb Meat Tenderness Based on the Study of Novel Isoforms and Alternative Splicing Regulation Pathways Using Iso-seq, RNA-seq and CTCF ChIP-seq Data. *Foods* **2022**, *11*, 1068. https://doi.org/10.3390/foods11081068

Academic Editors:
Mohammed Gagaoua and
Brigitte Picard

Received: 19 February 2022

Accepted: 4 April 2022

Published: 7 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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complex traits [11–13]. Although differentially expressed gene expression studies have been implemented for meat quality traits in sheep [14,15], studies focusing on the investigation of novel isoforms in sheep muscle are still scarce. Alternative splicing is the main mechanism to diversify isoforms and is regulated by numerous interacting components [16]. For example, the CCCTC-binding factor (CTCF) has been identified as a modulator of the alternative splicing process [17]. However, the role of CTCF in regulating alternative splicing to produce isoforms in sheep is largely unknown. In this context, using Iso-seq, RNA-seq and CTCF chromatin immunoprecipitation sequencing (ChIP-seq) data in sheep muscle can help to identify key isoforms associated with meat tenderness and to reveal the biological mechanisms of alternative splicing regulation to produce isoforms.

Tenderness is an important indicator of meat quality with the greatest consumer appreciation [18]. Crossbreeding is a common method for increasing sheep production. When using crossbreeding methods to increase sheep meat production, some meat quality traits, e.g., tenderness, show a significant difference across different cross-breed populations. Unveiling the genetic factors associated with tenderness is essential for guiding cross-breeding efficiently and profitably. Therefore, revealing the genetic processes that regulate meat quality between different cross-breeds is of great interest to the sheep industry.

The goals of the current study were to identify key isoforms associated with meat tenderness and to reveal the biological mechanisms of alternative splicing regulations to produce isoforms. To achieve these goals, differentially expressed isoforms (DEIs) were identified between two cross-bred populations with divergent meat tenderness by integrating Iso-seq and RNA-seq data. Then, we combined peaks detected from CTCF chromatin immunoprecipitation sequencing (ChIP-seq) data with DEIs to investigate the possible mechanism with which CTCF regulates the alternative splicing process. Our results have the potential to reveal novel isoforms associated with meat tenderness and uncover the possible regulating mechanism of alternative splicing to produce isoforms.

## 2. Materials and Methods

The animal experiment of this study was approved by the Experimental Animal Ethical Committee of Yangzhou University (File NO. 202103294). The overall experimental design of the current study is shown in Figure 1.

<figure><img src="image_39.png" /><figcaption>**Figure 1.** The overall experimental design. DEIs, differentially expressed isoforms.</figcaption></figure>
## 2.1. Animal Samples and Meat Traits

In the current study, a total of six unrelated 6-month-old male sheep, including three Dorper × Hu × Hu (DHH) and three Dorper × Dorper × Hu (DDH), were selected from a large population for sampling and measuring meat tenderness. Six male sheep were fed in the same environment from 2 months old to 6 months old. Six sheep were selected with similar live weights (DHH = 40.80 ± 4.57 kg, DDH = 41.50 ± 0.95 kg, Table 1) to rule out the effect of live weight on meat tenderness. Live weight was measured before slaughter (24 h in advance). After slaughter, *longissimus dorsi* from between the 12th and 13th ribs from six sheep were sampled within 30 min. Each muscle sample was packed into a 1.5 mL cryotube with triplicate. All the muscle samples were frozen in liquid and stored in a -80 °C refrigerator. After slaughter, carcass weight was measured by an electronic scale. Then, 45 min after slaughter, *longissimus dorsi* were cut after the 12th rib and the samples were cooked for 35 min at 71 °C [19]. Then, six 1 cm² cylinders were cut and all samples were tested using a meat tenderness analyzer (CL3, NanJing, CN). The difference of meat tenderness between DDH and DHH was tested by the t-test function in the basic environment of R 4.1.1 [20]. A *p* value smaller than 0.05 denotes significance.

<table><caption>**Table 1.** Meat traits of Dorper × Hu × Hu (DHH) and Dorper × Dorper × Hu (DDH) sheep.</caption><thead><tr><th rowspan="2">Meat Trait (Unit)</th><th colspan="2">Crossing Type</th><th rowspan="2">Statistic (t)</th><th rowspan="2">p Value</th></tr><tr><th>DDH <sup>1</sup> (n = 3)</th><th>DHH <sup>2</sup> (n = 3)</th></tr></thead><tbody><tr><td>Live weight (kg)</td><td>40.80 ± 4.57</td><td>41.50 ± 0.95</td><td>-0.2720</td><td>0.8094</td></tr><tr><td>Carcass weight (kg) <sup>3</sup></td><td>22.00 ± 2.27</td><td>23.70 ± 0.64</td><td>-1.2717</td><td>0.3161</td></tr><tr><td>Shear force (N) <sup>3</sup></td><td>83.20 ± 13.60</td><td>53.30 ± 8.39</td><td>3.2558</td><td>0.0406</td></tr></tbody></table>

## 2.2. RNA-Extraction, RNA-seq and Iso-seq

A Trizol reagent Kit (TaKaRa, Kusatsu, Shiga, Japan) was used to extract the total RNA from six muscle tissues. Then, Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA) and a 2100 Bioanalyzer (Agilent Technologies, Waldronn, Germany) were used to evaluate the RNA Integrity Number (RIN >7 passed quality control) and 28S/18S ratio (ratio > 1.0 passed quality control). After quality control of all six RNA samples, they were used for library construction. Following library quality control, six libraries were sequenced on the MGISEQ-2000 (BIG) platform [13].

Pooled RNA samples of six muscle tissues were used for Iso-seq. Briefly, cDNA was transcribed from a pooled RNA sample using the SMARTerTM PCR cDNA synthesis kit (Takara Biotechnology, Dalian, China). Following PCR amplification, PCR product purification, size selection (>1 kb), SMRTbell library construction, and library quality control, the library was sequenced on the PacBio sequencing platform. The raw Iso-seq data were processed using the SMRT Link v8.0 pipeline. Briefly, the BAM file was processed to obtain the circular consensus sequence (CCS). Full-length CCS reads with 5' and 3' cDNA primers and polyA were defined as full-length non-concatemer (FLNC) reads. FLNC reads were corrected by six clean RNA-seq data using LoRDEC v0.9 software [21]. The quality control of six RNA-seq data was implemented by SOAPnuke v2.1.0 [22].

## 2.3. Update Sheep Reference Annotation File

The pooled Iso-seq data were used to update the sheep reference annotation file. After correction, FLNC reads were mapped to the reference genome (http://ftp.ensembl.org/pub/release-102/fasta/ovis_aries_rambouillet/dna/, accessed on 29 March 2022) using GMAP [23]. Isoforms that met at least one of the following criteria were kept: first, an isoform was identified by at least two FLNC reads; second, an isoform was identified by only one FLNC read with a percentage identity (PID) of greater than 99%; third, all splicing sites in an isoform were identified by RNA-seq data; fourth, all alternative splicing

<footnote n="1">¹DDH denotes Dorper × Dorper × Hu. ²DHH denotes Dorper × Hu × Hu. ³These traits were measured on hot carcass.</footnote>
events in a identified isoform were also annotated by the reference genome annotation file. Isoforms that overlapped less than 20% of their length on the same strand were identified as distinct isoforms. A novel gene was defined as a gene locus overlapping less than 20% of their length with known genes. An isoform with a new emerged intron (exon) or a final splice site of 3' changed ends was defined as a novel isoform. In addition, the alternative splicing events were detected by the ASTalavista v3.2 software [24].

## 2.4. Differentially Expressed Isoform Detection and Functional Analyses

Bowtie2 was used to align short reads to novel annotation files [25]. The DESeq2 R Bioconductor package was used to identify DEIs between two groups [26]. An isoform with a fold change (FC) greater than two and a false discovery rate (FDR) smaller than 0.05 was defined as a significant DEI. All identified DEIs were blasted to GO database using Diamond [27] and blasted to the KEGG database using KOBAS [28] to extract their potential biological functions. The GOplot 1.0.2 R package was used to visualize GO enrichment results [29]. GO terms and KEGG pathways with an FDR smaller than 0.05 were regarded as having significant enrichment.

## 2.5. Validation of Differentially Expressed Isoforms

The isoform abundance of five DEIs were quantified for validating the result of identified DEIs by quantitative real-time PCR (qRT-PCR). Five isoform-specific paired premiers were designed by SnapGene® v2.3.2 software (from Insightful Science; available at snapgene.com) and were synthesized by Tsingke Biotechnology Co., Ltd. (Nanjing, China, Table S1). cDNA was synthesized using a FastKing gDNA Dispelling RT Super Mix (TIANGEN, Beijing, China). The qRT-PCR was performed on a CFX96 Connect™ Real-Time System (BIO-RAD, Hercules, CA, USA) using a 20 µL reaction volume, including 1 µL of cDNA in 10 µL of 2× TSINGKE Master qPCR Mix (SYBR Green I) (Tsingke, Nanjing, China), 0.8 µL (10 µm/µL) each of the forward and reverse primers, and 7.4 µL of distilled water. The abundance of the GAPDH was used as the control. Each biological sample was implemented in triplicate, and the 2−ΔΔCt method was used for calculating the relative expression level of isoforms.

## 2.6. CTCF ChIP-seq

We aimed to explore the possible regulating mechanism of CTCF in alternative splicing in sheep muscle. Two muscle samples with extreme meat tenderness were selected to implement CTCF ChIP-seq. CTCF ChIP-seq was implemented by a commercial sequencing provider (igenebook Technology Co., Ltd., Wuhan, China). Briefly, chromatin was crosslinked by formaldehyde, following nuclear processing, chromatin digestion, DNA-protein compound capture, decrosslinking of DNA-protein compounds, and the purification of DNA. Finally, the input and ChIP DNA samples were sequenced on the Illumina Hiseq X ten platform.

## 2.7. Bioinformatics Analysis of CTCF ChIP-seq

The quality of CTCF ChIP-seq data was evaluated by fastqc v0.11.5 [30]. The quality control of the raw CTCF ChIP-seq data was implemented by Trimmomatic v0.36 [31]. After quality control, clean reads were aligned to the sheep reference genome by BWA v0.7.1 [32]. The read distribution in different genomic regions was investigated using the ChIPseeker R Bioconductor package [33]. An Upset plot was plotted by the UpSetR R package [34]. Potential peaks were called by MACS v2.1.1 [35]. Differential peaks were detected by DiffBind v1.16.3 (https://bioconductor.org/packages/release/bioc/html/DiffBind.html, accessed on 29 March 2022). Peaks with an FDR < 0.05 and a Fold value >0 were defined as significant differential peaks. Motifs in significant differential peaks were predicted by HOMER v3 [36].
## 2.8. Overlapping between DEIs and Differential Peaks Called from ChIP-seq Data

The overlapping between genes with transcribed DEIs and genes located in differential peaks were investigated to explore the regulating role of CTCF in the alternative splicing process. The significance of overlapping was tested by the GeneOverlap R Bioconductor package (https://bioconductor.org/packages/release/bioc/html/GeneOverlap.html, accessed on 29 March 2022).

# 3. Results

## 3.1. Meat Traits

In the current study, a total of three meat traits were measured after slaughter. The mean value and standard deviation of three meat traits are documented in Table 1. Among these traits, shear force in DDH was significantly (p = 0.0406) higher than that in DHH. Other meat traits did not show a significant difference between DDH and DHH.

## 3.2. Update of Reference Genome Annotation File

To update the sheep genome annotation (Generic Feature Format, GFF), which is essential for accurately quantifying the abundance of isoforms, six RNA samples were pooled to implement Iso-seq. In total, 442,966 polymerase reads were produced by pooled Iso-seq (Table S2). After pre-processing, a total of 247,201 FLNC reads were kept for further analysis (Table S2). Novel genes and isoforms were identified according to the genomic position of each FLNC. In total, 18,959 gene loci (9184 known and 9775 novel) were identified (Figure S1a). Among these detected genes, 5104 (26.92%) loci had two or more transcripts (Figure S1b). In sheep muscle, 20,205 novel isoforms (57.51%) were transcribed from annotated genes (Figure S1c), followed by those transcribed from novel genes. Identified potential novel gene loci and novel isoforms as well as sheep reference annotation files (GFF file) were merged to obtain an updated genome annotation file (Table S3). Alternative splicing is the main mechanism to diversify isoforms. In this study, a total of 38,070 alternative splicing events were detected by pooled Iso-seq data (Figure S2). For example, seven novel isoforms in six pooled samples were identified in CAST due to alternative splicing (Figure 2).

<figure><img src="image_40.png" /><figcaption>**Figure 2.** Identified isoforms in CAST. The numbers at the top of the figure represent the genome coordinates. Each yellow horizontal line represents an isoform. Each yellow vertical dash represents an exon. Stars on the right denote the novel isoforms identified in the current study. Blue arrows denote the direction of transcription. Stars in right denote novel isoforms.</figcaption></figure>
### 3.3. Differentially Expressed Isoforms and Functional Analysis

Six RNA-seq data were aligned to the updated genome annotation file (Table S3) to identify the DEIs between DDH and DHH. As a result, a total of 624 DEIs were identified (Figure 3, Table S4). These 624 DEIs were transcribed from 492 genes, suggesting that some genes could produce more than one DEI. The most significant DEIs were X.351.3 (FHL1, FDR = 8.21 × 10⁻¹⁵) and 11.673.91 (MYH2, FDR = 8.21 × 10⁻¹⁵).

<figure><img src="image_41.png" /><figcaption>**Figure 3.** Volcano plot of differentially expressed isoforms (DEIs). X-axis denotes log2 (fold change). Y-axis denotes -log10 (adjusted *p* value). Red dots denote the up-regulated isoforms. Blue dots denote down-regulated isoforms.</figcaption></figure>

All detected DEIs were significantly (FDR < 0.05) enriched in 280 GO terms (Figure 4a, Table S5). The most significant GO term was contractile fiber (GO:0043292, FDR = 5.38 × 10⁻⁹, Figure 4a, Table S5). All detected DEIs were significantly (FDR < 0.05) enriched in 17 KEGG pathways (Figure 4b, Table S6). The most significant pathway was the glucagon signaling pathway (ko049222).

### 3.4. Validation for Target Isoforms

To validate the result of the identified DEIs, the qRT-PCR of five DEIs was implemented. Among these five isoforms, three DEIs identified from the transcriptome analysis were significantly expressed (Figure 5). The expression trends of these five isoforms, determined by qRT-PCR, were consistent with the transcriptome data.
<figure><img src="image_42.png" /><figcaption>**Figure 4.** Functional annotation of differentially expressed isoforms (DEIs); (a) gene ontology (GO) enrichment analysis; X-axis denotes z-score; Y-axis denotes -log10-adjusted *p* value); bubble size denotes the enriched gene numbers; (b) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis; X-axis denotes -log10 (adjusted *p* value); Y-axis denotes enriched pathway; bubble size denotes the enriched gene numbers; bubble color denotes the adjusted *p* value.</figcaption></figure>
<figure><img src="image_43.png" /><figcaption>**Figure 5.** Validation of the differentially expressed isoforms by qRT-PCR; Y-axis denotes isoforms; X-axis denotes isoform relative expression level; DDH denotes Dorper × Dorper × Hu; DHH denotes Dorper × Hu × Hu. Results were presented as the mean ± SEM, * *p* < 0.05, significant difference.</figcaption></figure>

3.5. CTCF ChIP-seq

Two muscle samples with divergent meat tenderness were selected for CTCF ChIP-seq.
After quality control, more than 40 M clean reads in each sample were obtained (Table S7).
All clean reads were mapped to the sheep reference genome with a mapping rate greater
than 96.46% in each sample (Table S7). Above half of the mapped reads were located in the
intergenic region (Figure 6), followed by an intron, promoter, exon, 3' untranslated regions
(UTR) and 5' UTR.

<figure><img src="image_44.png" /><figcaption>**Figure 6.** Read distribution of CTCF chromatin immunoprecipitation sequencing (ChIP-seq) among sheep genomes. According to the genome coordinates of reads, reads can be divided into six categories, including in intergenic region, intron region, promoter region, exon region, 3' untranslated region (UTR) and 5' untranslated region.</figcaption></figure>

A total of 4388 differential peaks were detected between two samples with divergent meat tenderness (Table S8). The greatest number of differential peaks were distributed in the intergenic genome region, followed by the intron region, promoter region, exon region, 5' UTR and 3' UTR (Figure 7). The motifs in the differential peaks were predicted. A total of 66 motifs were predicted in differential peaks (Table S9).
<figure><img src="image_45.png" /><figcaption>**Figure 7.** Distribution of differential peaks among the sheep genomes. (a) Up-regulated peaks; (b) down-regulated peaks. According to the genome coordinates of detected peaks, peaks can be divided into six categories, including in intergenic region, intron region, promoter region, exon region, 3' untranslated region (UTR) and 5' untranslated region. Dots in the bottom denote the peak categories. Black lines connecting the dots show peak numbers shared among several categories. The number above each bar shows the number of peaks for each category or shared category.</figcaption></figure>

### 3.6. Overlap between DEIs and Differential Peaks

To investigate the potential role of CTCF in regulating alternative splicing in sheep muscle, the overlapping analysis between genes with transcribed DEIs and genes located in differential peaks was implemented. A total of 86 overlapped genes were found (Figure 8). The overlapping *p*-value was smaller than 0.05, indicating that the number of overlapped genes was too great to have been by chance.

<figure><img src="image_46.png" /><figcaption>**Figure 8.** Venn plot of genes in differential peaks (**left**) and genes with transcribed differentially expressed isoforms (DEIs, **right**).</figcaption></figure>
## 4. Discussion

### 4.1. Update Sheep Reference Genome Annotation File

It has been reported that one possible method to quantify transcripts is mapping the reads to the annotated transcriptome [37]. In this study, pooled Iso-seq data were used to update the reference genome annotation file. As a result, 9775 possible novel genes and 30,513 novel isoforms were identified (Figure S1) in sheep muscle and were added to the sheep reference genome annotation file (Table S3). In a pig study, a total of 10,465 novel genes were identified by integrating Iso-seq and RNA-seq data [38]. Similarly, in sheep tail fat, a total of 9001 novel genes and 36,667 novel isoforms were detected using pooled Iso-seq data [13]. Our results are in line with these published works, which suggests that Iso-seq is a useful method to improve genome annotation in sheep.

Alternative splicing is the main mechanism to diversify isoforms. Here, we highlight an example of an alternative splicing event that may regulate meat tenderness. CAST is linked with meat tenderness across many farm animals [39]. In beef, it has been reported that alternative splicing [9] in CAST could regulate meat tenderness. In sheep, a previous study suggested that SNPs in CAST may regulate exon excision events in CAST (Oar.3.1, Chr5:93439378-93444596), which was identified by RNA-seq [40]. In the current study, this exon excision event was further validated by Figure 2. Taken together, all of these pieces of evidence indicate that alternative splicing in CAST regulated by SNPs may play an important role in the meat tenderness of sheep.

### 4.2. Novel Isoforms Linked with Meat Tenderness

The calpain system plays an essential role in meat tenderness [2]. Calpain3 (CAPN3), a member of the calpain system, is mainly expressed in skeletal muscle and may play an important role in the calpain system to regulate meat tenderness [6]. In the current study, a novel transcript, 7.524.18, transcribed from CAPN3, was identified as a DEI (Table S4), suggesting its potential role in meat tenderness.

Muscle fiber type also plays an important role in meat tenderness [41,42]. Muscle fiber type is related to the abundance of myosin type [43]. The ratio of slow and fast heavy chain myosin is linked to the ratio of type I and type II fibers [44]. In the current study, 12 DEIs transcribed from four myosin heavy chain genes were identified (11.673.36, 11.673.38, 11.673.91 and 11.673.53 transcribed from *MYH2*; ENSOART00020028698 transcribed from *MYH4*; 7.469.116, 7.469.121 and 7.469.129 transcribed from *MYH7*; 14.723.9, 14.723.27, 14.723.58 and 14.723.65 transcribed from *MYBPC2*; Table S4). Previous studies have reported that *MYH2*, *MYH4*, *MYH7* and *MYBPC2* play an important role in degerming muscle fiber types [45,46]. Overall, the myosin heavy chain-related isoforms were differentially expressed between DDH and DHH, suggesting that these isoforms may regulate meat tenderness directly or indirectly.

In addition, the amount of glycogen could affect meat tenderness [47]. In the current study, seven DEIs transcribed by two glycogen-related genes were detected (14.271.7 transcribed from *GYS1*; 21.138.57, 21.138.27, 21.138.13, 21.138.58, 21.138.9 and ENSOART00020017812 transcribed from *PYGM*). A previous study suggested that *PYGM* is related to shear force in cattle [48]. These DEIs might regulate tenderness by controlling glycogen content in muscles.

GO and KEGG enrichment analyses were implemented to extract the potential function of DEIs. In pigs, differentially expressed genes between fast and slow muscle were relevant to myofibril and contractile fiber GO terms. In the current study, DEIs were most significantly enriched in contractile fiber (GO:0043292) and myofibril (GO:0030016) GO terms. Our results provide further evidence for a relationship between muscle fiber type transformation, meat tenderness and DEIs. For the KEGG pathway enrichment analysis, a previous study suggested that a gene in the calcium signaling pathway (ko04020) is related to stiffness and affects the speed of fiber degradation during the meat aging process [49]. In the current study, 23 DEIs were significantly enriched in calcium signaling pathways, indicating their potential role in the meat aging process.
### 4.3. CTCF Might Regulate Alternative Splicing in Sheep Muscle

In the current study, CTCF ChIP-seq was implanted to investigate its potential role in regulating alternative splicing in sheep muscle with divergent meat tenderness. The main function of alternative splicing processes are to diversify isoforms [50]. In recent years, CTCF has been identified as a modulator of the alternative splicing process [17]. In this study, 86 overlapped genes were found between genes transcribing DEIs and genes in CTCF peaks, and the number of overlapped genes was too great to be accidental (Figure 7). These results suggest that CTCF may regulate alternative splicing in sheep muscle.

The CTCF-regulated alternative splicing mechanism can be divided into co-transcriptional, genomic and epigenetic mechanisms [17]. Here, our study example suggests that CTCF may regulate alternative splicing through the CTCF-mediated DNA methylation process in sheep muscle. In the current study, two DEIs, 3.1299.2 and 26.152.10, were transcribed from ANKRD23 (Table S4). In a cattle study, the ANKRD23-202 mRNA isoform, which is a splice form of the ANKRD, was defined as a DEI between the tough group and the tender group [49]. The result of a rabbit study suggested that ANKRD23 methylated in promoter and gene body regions is associated with exon skipping alternative splicing in the skeletal muscle [51]. Previous results suggest that CTCF could regulate alternative splicing by controlling DNA methylation [52–54]. In the current study, a CTCF peak was observed in ANKRD23 (Table S8). Taken together, CTCF may regulate ANKRD23 to produce different isoforms mediated by methylation.

As the first analysis of lamb meat tenderness based on the study of novel isoforms and alternative splicing regulation pathways, our study has its limitations. In our study, the sample size was relatively small, which may reduce the power to detect DEIs. In addition, tenderness is a parameter linked to DNA, exposition, individual characteristics, and a multitude of other factors that genetics cannot fully explain. Thus, in the future, we should increase sample size and consider more factors to investigate lamb meat tenderness. Nevertheless, our results provide the first insights about lamb meat tenderness based on the study of novel isoforms and alternative splicing regulation.

## 5. Conclusions

In this study, a total of 624 DEIs were identified between DDH and DHH. For example, isoform 7.524.18 transcribed from CAPN3 may be associated with meat tenderness. In addition, a total of 86 overlapped genes were found between genes with transcribed DEIs and genes in differential peaks identified by CTCF ChIP-seq. Among overlapped genes, ANKRD23 produces different isoforms that may be regulated by CTCF mediated by methylation. As preliminary research, our results identified novel isoforms associated with meat tenderness and revealed the possible regulating mechanism of alternative splicing to produce isoforms using bioinformatic analysis. In the future, more samples should be collected and more molecular experiments, e.g., TA clone, ChIP-qPCR, etc., should be implemented to improve these preliminary results.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods11081068/s1; Figure S1: Identified novel genes and isoforms: (a) Venn plot of identified genes and annotated genes; (b) frequency of the number of transcripts transcribed by a gene; (c) classification of identified isoforms; Figure S2: Classification of alternative splicing events; Table S1: Designed isoform-specific primers; Table S2: Data information of pooled isoform sequencing (Iso-seq) in sheep muscle; Table S3: Updated sheep genome annotation file; Table S4: Differentially expressed isoforms; Table S5: Gene ontology (GO) enrichment analysis of differentially expressed isoforms; Table S6: Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed isoforms; Table S7: Summarized information of chromatin immunoprecipitation sequencing (ChIP-seq); Table S8: Differential peaks between two muscle samples with divergent tenderness; Table S9: Predicated motifs in differential peaks.
**Author Contributions:** Conceptualization, W.S. and Z.Y.; methodology, Z.Y.; software, Z.Y.; validation, Z.Y., L.G., W.Z. and S.W.; formal analysis, Z.Y.; data curation, L.G., W.Z., S.W., X.C., X.L. and Z.Y.; writing—original draft preparation, Z.Y.; writing—review and editing, L.G., W.Z., S.W., X.L., X.C. and W.S.; visualization, Z.Y.; supervision, W.S.; project administration, W.S.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Major New Varieties of Agricultural Projects in Jiangsu Province (PZCZ201739), Natural Science Foundation of Jiangsu Province (BK20210811), National Natural Science Foundation of China (31872333, 32172689), National Natural Science Foundation of China-CGIAR (32061143036), The Projects of Domesticated Animals Platform of the Ministry of Science, Key Research and Development Plan (modern agriculture) in Jiangsu Province (BE2018354) and Jiangsu Agricultural Science and Technology Innovation Fund (CX(18)2003).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All of the raw sequencing data will be available through NCBI SRA: PRJNA745517.

**Acknowledgments:** We thank Mohammed Gagaoua (Teagasc Food Research Centre) for his valuable comments on the manuscript and Ruidong Xiang (The University of Melbourne) for his work in revising English phrasing.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Article

# A Simple and Reliable Single Tube Septuple PCR Assay for Simultaneous Identification of Seven Meat Species

Zhendong Cai<sup>1,2</sup>, Song Zhou<sup>1,2</sup>, Qianqian Liu<sup>3,*</sup>, Hui Ma<sup>1,2</sup>, Xinyi Yuan<sup>1,2</sup>, Jiaqi Gao<sup>1,2</sup>, Jinxuan Cao<sup>1,2</sup>
and Daodong Pan<sup>1,2,*</sup>

**Abstract:** Multiplex PCR methods have been frequently used for authentication of meat product adulteration. Through screening of new species-specific primers designed based on the mitochondrial DNA sequences, a septuple PCR method is ultimately developed and optimized to simultaneously detect seven species including turkey (110 bp), goose (194 bp), pig (254 bp), sheep (329 bp), beef (473 bp), chicken (612 bp) and duck (718 bp) in one reaction. The proposed method has been validated to be specific, sensitive, robust and inexpensive. Taken together, the developed septuple PCR assay is reliable and efficient, not only to authenticate animal species in commercial meat products, but also easily feasible in a general laboratory without special infrastructures.

**Keywords:** septuple PCR; adulteration; meat species; mitochondrial genes; multiplex PCR

## 1. Introduction

Meat authentication is an important concern to protect consumers from illegal and unwanted ingredients [1–4]. However, meat adulteration such as unlisted, mislabeled or fraudulent ingredients has frequently been reported around the world and has become a severe global issue [4,5]. Although some laws have been enacted for ensuring the quality and safety of meat products, adulteration is still widespread due to the purpose of economic pursuit [1,6]. Poultry meat (chicken, duck and goose), especially, is frequently adulterated to red meat due to their low cost of production in Chinese markets [1,7]. As is known, soy allergy has become an arising public health concern regarding food allergies, as a small amount of soybean may elicit allergic reactions in both children and adults [8]. Notably, there is increasing evidence supporting that meat may trigger allergic reactions, especially for sensitized patients, which may cause a severe health risk of infectious diseases, metabolic disorders and allergies [9]. In addition, meat adulteration could also violate religious concerns; as is known, meat products containing pork ingredients are not permitted by Kosher and Halal food laws [10,11]. Therefore, it remains a pressing need for identifying meat species with 100% accuracy in real-world foodstuffs.

In recent years, the techniques for authenticating meat species have been continuously evolving. Many analytical methods of biological, immunological, physical, chemical, anatomical and histological analyses have been developed [2–4,7,12]. Among them, protein-based methods are widely used to identify meat species in composite mixtures [5,13]. Proteins are likely to be degraded, denatured or damaged in processed meat products, limiting the accuracy of the identification of meat species in thermally treated foods [13,14]. In comparison, DNA-based analytical methods coupled with polymerase chain reaction

**Citation:** Cai, Z.; Zhou, S.; Liu, Q.; Ma, H.; Yuan, X.; Gao, J.; Cao, J.; Pan, D. A Simple and Reliable Single Tube Septuple PCR Assay for Simultaneous Identification of Seven Meat Species. *Foods* **2021**, *10*, 1083. https://doi.org/10.3390/foods10051083

Academic Editors:
Mohammed Gagaoua and
Brigitte Picard

Received: 14 March 2021

Accepted: 10 May 2021

Published: 13 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_47.png" /></figure>

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo University, Ningbo 315211, China; zhendongcai@hotmail.com (Z.C.); zhousongluchen@163.com (S.Z.); m17805843103@163.com (H.M.); yxy13065810608@163.com (X.Y.); caidan1389@126.com (J.G.); caojinxuan@nbu.edu.cn (J.C.)</footnote>

<footnote n="2">² Key Laboratory of Animal Protein Deep Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China</footnote>

<footnote n="3">³ Institute of Environmental Research at Greater Bay Area, Guangzhou University, Guangzhou 510006, China</footnote>

<footnote>* Correspondence: csuliuqian@foxmail.com (Q.L.); daodongpan@163.com (D.P.)</footnote>
(PCR) present a reliable alternative to protein-based methods in the discrimination and mislabeling detection of meat species, as DNA molecules possess high stability and are present in every type of cell [4,5]. Both multiplex and real-time PCR techniques are highly specific and efficient in the identification of meat adulteration [15]. Real-time PCR techniques are widely used to quantify the amount of a target sequence in a reaction system. The levels of PCR amplification are monitored in real time, once per cycle, by measuring specific fluorescence signals, whose intensities reflect the amount of PCR product [16]. With the progress of molecular biology in recent years, multiplex real-time PCR techniques depending on melting curve analysis have been developed and widely adopted in the identification of meat species, and they have characteristics such as time saving, high specificity and high sensitivity [17–20]. Collectively, real-time PCR analysis shows more detailed information with regard to the identification and quantification of meat species. However, accurate quantification can only be achieved with a proper reference material because the matrix may interfere with the amplification process [21], indicating that quantification of meat fractions in real-world foodstuffs is difficult, and the results may not truly correlate to the recipe of the meat products. In comparison, multiplex PCR assay can be easily implemented with minimum effort, but much gain, to verify the identification of meat species. Recently, many studies have also constructed multiplex PCR with electrophoresis analysis to authenticate meat species with satisfying results [1]. Although much is known about multiplex PCR as duplex, triplex, tetraplex, pentaplex (quintuple) and hexaplex (sextuple) PCRs, little information is available on a multiplex PCR authenticating more than six animal species simultaneously.

Mitochondrial DNA possesses high copy numbers per cell and strong stability, which ensures a low limit of detection and its availability in both raw and cooked meat products, and it has been broadly adopted for PCR protocols [22,23]. For example, cytochrome *b* gene, D-loop, 12S and 16S rRNA genes, ATPase subunits 8 and 6 genes, and NADH dehydrogenase genes are common targets for identifying meat species [21,24,25]. All data provide reliable evidence for the roles of mitochondrial DNA sequences in animal species identification. Using mitochondrial DNA sequences obtained from turkey, goose, pig, sheep, beef, chicken and duck, we designed a set of primer pairs that specifically amplified for seven species with differential lengths through PCR assays. We next performed the specific, sensitive and cost-effective detection of the indicated primers through simplex and multiplex PCR assays. Through screening, this study develops a septuple PCR assay for identifying seven ingredients of turkey, goose, pig, sheep, beef, chicken and duck simultaneously in commercial foodstuffs.

## 2. Materials and Methods

### 2.1. Preparation of Meat Samples and DNA Extraction

Fresh meat samples of turkey, goose, pig, sheep, beef, chicken and duck were purchased from local retailers and markets and transported on ice to the laboratory for immediate processing. Samples were stored at -20 °C to prevent DNA degradation. Total DNA was extracted from various meat samples using the EasyPure® Genomic DNA Kit (Beijing Trans Gen Biotech Co., Ltd., Beijing, China) following the manufacturer's instructions. DNA concentration was determined by a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA).

### 2.2. Design of Species-Specific Primers

Primers were designed by targeting mitochondrial DNA sequences based on both high divergence and conservation within the species. As shown in Table 1, sequences of 16S rRNA gene of turkey (GenBank Accession No. EF153719.1), 16S rRNA gene of goose (KJ124555.1), cytochrome *c* oxidase subunit I gene of pig (KJ746666.1), cytochrome *c* oxidase subunit I gene of sheep (KP702285.1), 16S rRNA gene of cattle (MN714195.1), cytochrome *b* gene of chicken (MK163565.1) and 12S rRNA gene of duck (MK770342.1) were obtained from the National Centre of Biotechnology Information (NCBI) database. Next, the MEGA6 alignment tool
was employed for identifying the conservative and variable regions. Using Oligo 7.0 and BLAST programs (www.ncbi.nlm.nih.gov/blast/ accessed on 1 April 2021), primers were newly designed according to their physical parameters, such as melting temperature, secondary structures, self-complementarity and cross-reactivity. The primer pairs were synthesized by Shanghai Sangon Biological Engineering Technology & Services Co., Ltd. (Shanghai, China). To determine the mismatch between the target and nontarget species, each set of primers was in silico screened with 13 land animals: turkey (Meleagris gallopavo), goose (Anser cygnoides), pig (Sus scrofa), cattle (Bos taurus), sheep (Ovis aries), chicken (Gallus gallus), duck (Anas platyrhynchos), horse (Equus caballus), camel (Camelus bactrianus), ostrich (Struthio camelus), dog (Canis lupus), rabbit (Oryctolagus cuniculus), cat (Felis catus) and 3 aquatic species, namely, small yellow croaker (Larimichthys polyactis), tuna (Thunnus orientalis) and black carp (Mylopharyngodon piceus), using ClustalW software. The final specificity of each primer pair was examined through PCR assays against templates of all species mentioned above.

<table><caption>**Table 1.** Oligonucleotide primers for meat species used in this study.</caption><thead><tr><th>Primers</th><th>Genes</th><th>Sequence (5'-3' Direction)</th><th>Amplicons (bp)</th><th>Reference or Source</th></tr></thead><tbody><tr><td>Turkey</td><td>16S rRNA</td><td>CTCTAGCCCAACCACCCAT</td><td>110</td><td>this study</td></tr><tr><td>Goose</td><td>16S rRNA</td><td>GCGCCTAAGGTCTTTTCTATCAC</td><td>194</td><td>this study</td></tr><tr><td>Pig</td><td>cytochrome c oxidase subunit I</td><td>TTAGACGCGATAGAGACCCCA</td><td>254</td><td>this study</td></tr><tr><td>Sheep</td><td>cytochrome c oxidase subunit I</td><td>GTTCGCTCTCTTTAACTGCTTG</td><td>329</td><td>this study</td></tr><tr><td>Beef</td><td>16S rRNA</td><td>CAGCCCGGAACCCTACTTG</td><td>473</td><td>this study</td></tr><tr><td>Chicken</td><td>cytochrome b</td><td>GTTCATCCAGTACCCGCTCC</td><td>612</td><td>this study</td></tr><tr><td>Duck</td><td>12S rRNA</td><td>AGATATCGGCACCTTTACCTTC</td><td>718</td><td>this study</td></tr><tr><td>Eukaryotes</td><td>12S rRNA</td><td>CTGCTCCGGCCTCAACCAT</td><td>456</td><td>[26]</td></tr><tr><td>Eukaryotes</td><td>16S rRNA</td><td>GTGCCTGATAATACTCTGACCAC</td><td>240</td><td>[27]</td></tr><tr><td>Eukaryotes</td><td>18S rRNA</td><td>CACCCCAACCGAAACTACCAA</td><td>99</td><td>[28]</td></tr><tr><td></td><td></td><td>TTTCGGCTCCCTATTAGCAGTC</td><td></td><td></td></tr><tr><td></td><td></td><td>AGTATGAGAGTTAAGCCCAGA</td><td></td><td></td></tr><tr><td></td><td></td><td>TGCCCTCAATAGCCTTCACC</td><td></td><td></td></tr><tr><td></td><td></td><td>CATACTTCTTTCCGTGTTGCC</td><td></td><td></td></tr><tr><td></td><td></td><td>CAACTGGGATTAGATACCCCACTAT</td><td></td><td></td></tr><tr><td></td><td></td><td>GAGGGTGACGGGCGGTGTGT</td><td></td><td></td></tr><tr><td></td><td></td><td>AAGACGAGAAGACCCTATGGA</td><td></td><td></td></tr><tr><td></td><td></td><td>GATTGCGCTGTTATCCCTAGGGTA</td><td></td><td></td></tr><tr><td></td><td></td><td>AGGATCCATTGGAGGGCAAGT</td><td></td><td></td></tr><tr><td></td><td></td><td>TCCAACTACGAGCTTTTTAACTGCA</td><td></td><td></td></tr></tbody></table>

## 2.3. Simplex and Multiplex PCR

To develop a multiplex PCR method, simplex PCR was firstly carried out for each of the target species with their own primers to ensure that each target was amplified. PCR amplification (in one reaction of 25 µL, including 2.5 µL of 10 × EasyTaq® Buffer, 2 µL of 2.5 mM dNTPs, 0.5 µL of EasyTaq DNA Polymerase, 0.5 µL of 10 µM each primer, and 0.01–10 ng genomic DNA of each species) was achieved using EasyTaq® DNA Polymerase kit (TransGen Biotech Co., Ltd., Beijing, China). The reaction was initiated by a 5 min denaturation at 94 °C, followed by 34 cycles of denaturation at 94 °C for 30 s, annealing at 63 °C for 30 s, elongation at 72 °C for 45 s and a final elongation at 72 °C for 5 min. For universal primer pairs, the annealing was set at 56 °C. After simplex PCR assays for each species was achieved, a septuple PCR assay was developed. The 4% agarose gels were visualized and subsequently photographed in a Bio-rad GelDoc 1000 gel documentation system.

## 2.4. Test of Primers' Specificity, Sensitivity and Reproducibility

The specificity of species-specific primers was corroborated by using template DNA isolated from all species (turkey, goose, pig, cattle, sheep, chicken, duck, horse, camel, ostrich, dog, rabbit, cat, small yellow croaker, tuna, black carp). In the preliminary phase
of this experiment, simplex and septuple PCRs were respectively performed by using the DNA extracted from raw animal species. The PCR product was run on agarose gel and then visualized for the proper amplification.

The sensitivity of septuple PCR assay was confirmed by serial dilutions of the pre-mixed DNA templates of all target species indicated, starting with 10 ng and progressing downward in one reaction. Seven concentrations (10 ng to 0.01 ng) of the target templates were used for PCR amplification determining the minimal concentration detected. PCR fragments were run on 4% agarose gel to confirm the limit of detection.

To assess the reproducibility, template DNA isolated from raw, boiled (97–99 °C, 30 min), autoclaved (121 °C, 150 kPa for 15 min) and microwave-cooked (750 W, 10 min, 119–121 °C) meat samples were individually analyzed by using the simplex PCR. The PCR product was run on agarose gel.

## 2.5. Commercial Samples

Using the present multiplex PCR method, 60 raw or thermally processed meat products including meat balls (15), meat slices (13), kebab (10), sausages (5), cutlets (10), jerky (2) and breast (5) were purchased from retail markets and supermarkets in Ningbo City, PR China, as well as online supermarket platforms, which were used for assessing the authentication of meat species. Details of the samples are listed in Table 2. All samples of meat balls, slices, kebabs, sausages, cutlets and breasts were raw materials with mechanical processing but not heat treatment, while two jerky samples, within turkey, were subjected to heat processing.

<table><caption>Table 2. Results of multiplex PCR assay performed on commercial meat products.</caption><thead><tr><th rowspan="2">Products</th><th rowspan="2">No</th><th rowspan="2">Labelled</th><th colspan="7">Detected Species</th><th rowspan="2">Adulteration</th></tr><tr><th>Turkey</th><th>Goose</th><th>Pig</th><th>Sheep</th><th>Beef</th><th>Chicken</th><th>Duck</th></tr></thead><tbody><tr><td>Beef</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>5 (33.3%)</td></tr><tr><td>meat balls</td><td>5</td><td>beef</td><td>1/5<sup>b</sup></td><td>—</td><td>1/5<sup>a</sup>, 1/5<sup>b</sup></td><td>—</td><td>5/5</td><td>1/5<sup>a</sup></td><td>—</td><td></td></tr><tr><td>meat slices</td><td>5</td><td>beef</td><td>—</td><td>—</td><td>1/5</td><td>—</td><td>5/5</td><td>—</td><td>—</td><td></td></tr><tr><td>kebab</td><td>5</td><td>beef</td><td>—</td><td>—</td><td>2/5</td><td>—</td><td>5/5</td><td>—</td><td>—</td><td></td></tr><tr><td>Mutton</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>6 (40.0%)</td></tr><tr><td>meat balls</td><td>5</td><td>mutton</td><td>—</td><td>—</td><td>1/5<sup>a</sup>, 1/5<sup>b</sup></td><td>5/5</td><td>—</td><td>1/5<sup>a</sup></td><td>1/5<sup>b</sup></td><td></td></tr><tr><td>meat slices</td><td>5</td><td>mutton</td><td>—</td><td>—</td><td>2/5</td><td>5/5</td><td>—</td><td>—</td><td>—</td><td></td></tr><tr><td>kebab</td><td>5</td><td>mutton</td><td>—</td><td>—</td><td>2/5</td><td>5/5</td><td>—</td><td>—</td><td>—</td><td></td></tr><tr><td>Pork</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>4 (26.7%)</td></tr><tr><td>meat balls</td><td>5</td><td>pig</td><td>—</td><td>1/5<sup>b</sup></td><td>5/5</td><td>—</td><td>—</td><td>1/5<sup>a</sup>, 1/5<sup>b</sup></td><td>1/5<sup>a</sup></td><td></td></tr><tr><td>sausages</td><td>5</td><td>pig</td><td>—</td><td>—</td><td>5/5</td><td>—</td><td>—</td><td>1/5<sup>a</sup></td><td>1/5<sup>b</sup></td><td></td></tr><tr><td>cutlets</td><td>5</td><td>pig</td><td>—</td><td>—</td><td>5/5</td><td>—</td><td>—</td><td>—</td><td>—</td><td></td></tr><tr><td>Turkey</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>cutlets</td><td>5</td><td>turkey</td><td>5/5</td><td>—</td><td>—</td><td>—</td><td>—</td><td>1/5</td><td>—</td><td>1 (6.7%)</td></tr><tr><td>meat slices</td><td>3</td><td>turkey</td><td>3/3</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td></td></tr><tr><td>breast</td><td>5</td><td>turkey</td><td>5/5</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td></td></tr><tr><td>jerky</td><td>2</td><td>turkey</td><td>2/2</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td></td></tr></tbody></table>

A horizontal line (—) denotes no PCR product detected. In each row, the meat samples labeled with same letter (a or b) represent the identical meat samples, while different letters indicate a difference in meat samples.

# 3. Results

## 3.1. Specificity Assays of Simplex PCR

To determine the species-specific primers, we designed many pairs of primers for each species as candidates by using Oligo 7.0 and BLAST programs. Each set of primers was compared against 16 species (turkey, goose, pig, cattle, sheep, chicken, duck, horse, camel, ostrich, dog, rabbit, cat, small yellow croaker, tuna and black carp) by simplex PCR assays (data not shown). Through gel electrophoresis, we ultimately selected the primer
pairs for each species in Table 1. PCR fragments showed distinguishable bands with the predicted size of 110, 194, 254, 329, 473, 612 and 718 bp for turkey, goose, pig, sheep, beef, chicken and duck species, respectively (Figure 1A). Three pairs of universal eukaryotic primers, which target 12S rRNA, 16S rRNA and 18S rRNA genes with individual 456, 240 and 99 bp PCR fragments in all meat species, were used as positive controls for ensuring the quality of template DNAs in one PCR reaction (Figure 1B). To further test the efficiency and specificity of primers, simplex PCRs were carried out using a DNA mixture of all seven meat species. In these experiments, each set of species-specific primers yielded the expected PCR fragment by using only the template DNA mixture of seven meat species, but not with nontarget species (Figure 1C), further confirming that the new primers were highly specific for the target species.

<figure><img src="image_48.png" /><figcaption>**Figure 1.** Specificity assays of simplex PCR. (A) Gel image of the products generated by PCR amplification with species-specific primers for turkey, goose, pig, cattle, sheep, chicken and duck using corresponding genomic DNA as a template, respectively. (B) As positive controls, gel image of the PCR products generated after amplification with premixed universal eukaryotic primer pairs of 12S rRNA, 16S rRNA and 18S rRNA genes for all meat species. (C) Gel image of the products through simplex PCR amplification using species-specific primers for turkey, goose, pig, cattle, sheep, chicken and duck. CM, a complete mixture of turkey, goose, pig, cattle, sheep, chicken and duck; 1–7, a complete DNA mixture except target species DNA. Lane M is ladder DNA.</figcaption></figure>

## 3.2. *Sensitivity Assays of Septuple PCR*

A septuple PCR system was constructed by using seven sets of species-specific primers. To validate the sensitivity of the multiplex PCR assay, extracted DNA of each target species was serially diluted (10, 5, 1, 0.5, 0.1, 0.05 and 0.01 ng). PCR products were subsequently run on an agarose gel to assess the sensitivity. As can be seen from Figure 2A,
the expected bands of seven meat species were obtained by multiplex PCR under the conditions of all tested concentrations (10–0.01 ng). In accordance with that of gel-view, each electropherogram clearly represented seven peaks corresponding to the seven different bands displayed in the gel-view (Figure 2B). Both intensities of bands and peaks were dramatically decreased in a concentration-dependent manner. However, even at the concentration of 0.01 ng per reaction, some PCR products of meat species can be clearly recognized in Figure 2A,B. Thus, the limit of detection of the developed septuple PCR assay was concluded to be 0.01–0.05 ng DNA.

<figure><img src="image_49.png" /><figcaption>**Figure 2.** Sensitivity of the developed septuple PCR assay. (A) Gel image of the products generated after multiplex PCR amplifications of premixed DNA templates of all species (10, 5, 1, 0.5, 0.1, 0.05, 0.01 ng) with primers of seven meat species mixtures including turkey, goose, pig, cattle, sheep, chicken and duck. (B) The corresponding electropherograms of gel image (A). Lanes 1–7 are presented with labels (10, 5, 1, 0.5, 0.1, 0.05, 0.01) in (A). Lane M is ladder DNA.</figcaption></figure>

### 3.3. Validation of Reproducibility Assay in Thermally Processed Meat

To assess the efficiency of designed primers in detecting thermally processed meat, three different heat treatment processes were selected to treat raw meat samples as described in Section 2.4. The quality of template DNA extracted from processed meat samples was examined by simplex PCR assays. As shown in Figure 3A–D, using DNA extracted from raw, boiled, autoclaved and microwave-cooked meat samples, PCR amplification of turkey, goose, pig, sheep, beef, chicken and duck species generated the expected PCR
products with 100% accuracy in meat authentication, indicating that our designed primers
can be successfully employed for authenticating animal species in processed meat products.

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /><figcaption>**Figure 3.** Gel image of the PCR products generated by simplex PCR amplifications with turkey, goose, pig, cattle, sheep, chicken and duck DNA extracted from raw (A), boiled (B), autoclaved (C) and microwave-cooked meat samples (D) using each species-specific primer pair. Lane M is ladder DNA.</figcaption></figure>

3.4. Application of Multiplex PCR Assay on Commercially Processed Meat Products

The real-world food products were examined using the developed septuple PCR.
The survey was conducted with 60 commercial samples of beef, mutton, pork and turkey
(15 samples each). As shown in Figure 4 and summarized in Table 2, most of the samples
had the same ingredients as labeled, without contamination. However, 5 of 15 (33.3%)
beef samples, 6 of 15 (40.0%) mutton samples, 4 of 15 (26.7%) pork samples and 1 of
15 (6.7%) turkey samples contained some unlisted meat species. The survey revealed
that inexpensive chicken, duck and pork meats were frequently adulterated products.
The results further corroborated the efficiency of the developed septuple PCR assay in
identification of commonly consumed meats.
<figure><img src="image_54.png" /><figcaption>**Figure 4.** Analysis of commercial foodstuffs using the developed septuple assay. Gel image of the fragments generated by multiplex PCR amplifications using DNA obtained from commercial meat products with premixed primers for seven meat species including turkey, goose, pig, cattle, sheep, chicken and duck.</figcaption></figure>

**4. Discussion**

Frequent meat frauds have aroused significant social attention because adulteration risks food safety, breaches market rules and even threatens public health [8]. In recent years, adulteration practice has been ingeniously applied to treated meat products showing
similar morphological and physical appearance to pure meat. Nowadays, PCR-based techniques are the effective methods for species authentication. Real-time PCR techniques and microchip electrophoresis-dependent multiplex PCR methods require special infras- tructures [11,29,30]; multiplex PCR assays through simple agarose gel analysis minimizes the cost drastically on a large scale and can be easily carried out to verify the identity of ingredients in foodstuffs [6,31,32]. As seen in Table 3, much is known about multiplex PCR assays that simultaneously verify two to six meat ingredients in one reaction. Relatively little information is available on multiplex PCR methods for authenticating more meat species simultaneously. Although one study authenticated 10 animal species (beef, sheep, pork, chicken, turkey, cat, dog, mouse, rat, human), it was achieved by two tube multiplex assays, where every five animal species were verified by a pentaplex PCR assay in one reaction [33]. Similarly, using two tube independent pentaplex PCR assays with ten pairs of primers, 14 animal species including cattle, donkey, *Canidae* (dog, fox, raccoon-dog), deer and horse, pig, *Ovis* (sheep, goat), poultry (chicken, duck), cat and mouse were detected through chip electrophoresis; however, the multiplex PCR failed to accurately distinguish sheep and goat within Ovis, dog, fox and raccoon-dog within Canidae, and chicken and duck within poultry [11]. Therefore, there is still a lack of more efficient detection methods with low cost for supervising more meat content. The goal of the present study was to de- velop a multiplex PCR method for reliable and efficient identification of ruminant, poultry and pork materials.

The choice of animal species was considered based on actual adulteration cases, with a higher practicability in Chinese markets. We found that multiplex PCR with increased species-specific primers in one reaction led to more opportunities of cross-reactivity with each other, or generated unexpected bands, which may limit the availability of multi- plex PCR for verifying more animal species. To provide a multiplex PCR method that detects more animal species in a single assay platform, we designed many sets of primers throughout target mitochondrial DNA sequences such as cytochrome b gene, D-loop, 12S and 16S rRNA genes, ATPase subunits 8 and 6 genes and NADH dehydrogenase genes using Oligo 7.0 and Primer-BLAST programs. Through screening species-specific primer pairs, a species-specific septuple PCR method was ultimately developed and optimized to simultaneously detect turkey (110 bp), goose (194 bp), pig (254 bp), sheep (329 bp), beef (473 bp), chicken (612 bp) and duck (718 bp) in one reaction. To ensure the quality of template genomic DNA in one PCR reaction, a universal eukaryotic primer set that amplifies a bigger PCR fragment than that of all meat species tested should be chosen as the preferred positive control. However, to our knowledge, little information is available on a universal eukaryotic primer set amplifying the fragment with more than 700 bp length. As alternatives, we chose three pairs of universal eukaryotic primers, which target different mitochondrial DNA sequences, including 12S rRNA, 16S rRNA, 18S rRNA genes, and amplifies distinguishable 456, 240 and 99 bp PCR fragments in all meat species, respec- tively [26–28]. In addition, the control primer set should be inserted in the multiplex assay; these PCR fragments were too close to that of turkey (110 bp), pig (254 bp) and beef (473 bp) to discriminate each other. Accordingly, these universal primer pairs were used in a single PCR in this study. Figure 1B shows the expected bands of each primer set in all meat species, implying the high quality of genomic DNA used in this study.
<table><caption>**Table 3.** Comparative analysis of recently published multiplex PCR assays for the identification of meat species.</caption><thead><tr><th>Multiplex PCR Type</th><th>Sp. No <sup>a</sup></th><th>Detection Items</th><th>Detection Limit</th><th>Detection Method <sup>b</sup></th><th>Reference or Source</th></tr></thead><tbody><tr><td>Septuple</td><td>7</td><td>turkey, goose, pig, sheep, beef, chicken, duck</td><td>0.01–0.05 ng DNA</td><td>Gel</td><td>This study</td></tr><tr><td>Multiplex</td><td>4</td><td>ruminant, poultry, pork, and donkey</td><td>0.01–0.1 ng/µL DNA</td><td>Gel</td><td>[25]</td></tr><tr><td>Hexaplex</td><td>6</td><td>chicken, cow/buffalo, sheep/goat, horse/donkey, pork, dog</td><td>0.03–0.05 ng DNA</td><td>Gel</td><td>[31]</td></tr><tr><td>Multiplex</td><td>5</td><td>sheep/goat, bovine, chicken, duck, pig</td><td>0.5 ng DNA</td><td>Gel</td><td>[6]</td></tr><tr><td>Multiplex</td><td>2</td><td>cattle, buffalo</td><td>2.23–2.31 ng/µL DNA</td><td>Gel</td><td>[34]</td></tr><tr><td>Quadruple</td><td>4</td><td>fox, mink, or raccoon in beef and mutton</td><td>1% for each species</td><td>Gel</td><td>[35]</td></tr><tr><td>Pentaplex</td><td>5</td><td>dog, duck, buffalo, goat, sheep</td><td>0.1–0.32 ng DNA</td><td>Gel</td><td>[21]</td></tr><tr><td>Multiplex (two-tube)</td><td>14</td><td>cattle, donkey, Canidae (dog, fox, raccoon-dog), deer and horse, pig, Ovis (sheep, goat), poultry (chicken, duck), cat, mouse</td><td>0.02–0.2 ng DNA</td><td>Chip</td><td>[11]</td></tr><tr><td>Quadruplex</td><td>4</td><td>chicken, mutton, beef, pork</td><td>16 pg DNA, 0.01% of each species</td><td>Gel</td><td>[36]</td></tr><tr><td>Multiplex (two-tube)</td><td>10</td><td>beef, sheep, pork, chicken, turkey, cat, dog, mouse, rat, human</td><td>30 pg DNA</td><td>Gel</td><td>[33]</td></tr><tr><td>Tetraplex</td><td>3</td><td>pig, cattle, fish, eukaryotic18S rRNA</td><td>0.001–0.1 ng DNA</td><td>Gel</td><td>[37]</td></tr><tr><td>Hexaplex</td><td>6</td><td>horse, soybean, sheep, poultry, pork, cow</td><td>0.01% for each species</td><td>Gel</td><td>[38]</td></tr><tr><td>Octuplex</td><td>8</td><td>dog, chicken, cattle, pig, horse, donkey, fox, and rabbit</td><td>0.05 ng/µL DNA</td><td>Gel</td><td>[27]</td></tr><tr><td>Multiplex</td><td>3</td><td>chicken, duck and goose</td><td>0.05 ng DNA, 1% for each species</td><td>Gel</td><td>[39]</td></tr><tr><td>Multiplex</td><td>5</td><td>cat, dog, pig, monkey, rat</td><td>0.01–0.02 ng DNA</td><td>Chip</td><td>[24]</td></tr><tr><td>Quadruple</td><td>4</td><td>beef, pork, mutton, duck</td><td>0.1 ng DNA</td><td>Gel</td><td>[40]</td></tr></tbody></table>

Through the specificity test, we validated that the primers were highly specific to each of particular species and had no cross-reactivity, at least with the 15 animal species tested. The detection limit of this particular assay on reference meat samples was 0.01–0.05 ng, indicating that this method is highly sensitive and reliable. Using the DNA isolated from raw, boiled, autoclaved and microwave-cooked samples of seven meat species, simplex PCR assays generated all expected PCR products, suggesting that PCR assay with our primers had a high reproducibility in processed meat samples. Most importantly, multiplex PCR assay on commercially available processed meat products revealed that inexpensive chicken, duck and pork meats were adulterated products (Table 2 and Figure 4). Consistent with previous reports, inexpensive poultry meat readily evades visual detection and is frequently adulterated into other meat products, in particular, processed beef, mutton and pork [41–43]. Perhaps, economically driven thoughts of manufacturers or peddlers are a critical factor for the replacement of expensive, high-quality meat with inferior and low-cost ones. Collectively, the developed septuple PCR assay is not only reliable and efficient but is also a sensitive detection method for the identification of meat species in actual adulteration events. However, vegetable proteins such as soybean are found to a substitute ingredient for muscle proteins, due to their low cost of production [8,38,44]. In addition, some surveys demonstrate that inexpensive fish species are adulterated into meat products [44,45]. Therefore, we still cannot exclude the possibility that vegetable proteins and fish sources may be present in commercial meat products. Considering the

<footnote><sup>a</sup> Species number; <sup>b</sup> Chip, microchip electrophoresis; Gel, agarose gel electrophoresis.</footnote>
fact that multiplex PCR with increased species-specific primers in one reaction may cause
more opportunities of cross-reactivity with each other, or generated unexpected bands, a
more efficient method for identification of meat adulteration should be developed in future
study.

**5. Conclusions**

The aim of this study is to provide reliable adulteration detection, by means of septuple PCR, which can simultaneously authenticate seven animal species of turkey, goose, pig, sheep, beef, chicken and duck. The assay is also quite sensitive to enable detection of 0.01–0.05 ng DNA templates for each species per reaction, thus making it qualified for authenticating meat species in commercial, real-world foodstuffs. By simple agarose gel analysis, without expensive equipment or a high level of technical skill, this septuple PCR method could be more broadly used for detecting sources of meat species in foodstuffs in which adulteration is suspected.

**Author Contributions:** Z.C., Q.L. and D.P. contributed to the conceptualization, methodology, supervision, investigation and data curation; Z.C. and D.P. contributed to the funding acquisition; S.Z. and H.M. contributed to the software and data curation; X.Y., J.G. and J.C. contributed to the project administration and methodology; Z.C. and Q.L. contributed to the writing—original draft. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financially supported by the National Natural Science Foundation of China (NSFC) (31901668 and 31972048), Scientific Research Fund of Zhejiang Provincial Education Department (Y201940932), the Natural Science Foundation of Ningbo (2019A610436), School Research Project in Ningbo University (XYL19011) and the Open Project Program of Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University (BTBU) (SPFW2020YB13).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors have declared no conflict of interest. This paper is our original un-
published work; it has not been published previously and is not under consideration for publication
elsewhere, in whole or in part. All authors of this paper have read and approved the final, submitted
version.

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<figure><img src="image_55.png" /></figure>
Article

# Interlaboratory Validation of a DNA Metabarcoding Assay for Mammalian and Poultry Species to Detect Food Adulteration

Stefanie Dobrovolny<fnref n="1" />, Steffen Uhlig<fnref n="2" />, Kirstin Frost<fnref n="2" />, Anja Schlierf<fnref n="2" />, Kapil Nichani<fnref n="2" />, Kirsten Simon<fnref n="2" />, Margit Cichna-Markl <fnref n="3" />,* and Rupert Hochegger <fnref n="1" />,*

**Abstract:** Meat species authentication in food is most commonly based on the detection of genetic variations. Official food control laboratories frequently apply single and multiplex real-time polymerase chain reaction (PCR) assays and/or DNA arrays. However, in the near future, DNA metabarcoding, the generation of PCR products for DNA barcodes, followed by massively parallel sequencing by next generation sequencing (NGS) technologies, could be an attractive alternative. DNA metabarcoding is superior to well-established methodologies since it allows simultaneous identification of a wide variety of species not only in individual foodstuffs but even in complex mixtures. We have recently published a DNA metabarcoding assay for the identification and differentiation of 15 mammalian species and six poultry species. With the aim to harmonize analytical methods for food authentication across EU Member States, the DNA metabarcoding assay has been tested in an interlaboratory ring trial including 15 laboratories. Each laboratory analyzed 16 anonymously labelled samples (eight samples, two subsamples each), comprising six DNA extract mixtures, one DNA extract from a model sausage, and one DNA extract from maize (negative control). Evaluation of data on repeatability, reproducibility, robustness, and measurement uncertainty indicated that the DNA metabarcoding method is applicable for meat species authentication in routine analysis.

**Keywords:** DNA metabarcoding; animal species; species identification; NGS; food adulteration; validation; interlaboratory ring trial

**Citation:** Dobrovolny, S.; Uhlig, S.; Frost, K.; Schlierf, A.; Nichani, K.; Simon, K.; Cichna-Markl, M.; Hochegger, R. Interlaboratory Validation of a DNA Metabarcoding Assay for Mammalian and Poultry Species to Detect Food Adulteration. *Foods* **2022**, *11*, 1108. https://doi.org/10.3390/foods11081108

Academic Editors:
Mohammed Gagaoua and
Brigitte Picard

Received: 5 March 2022

Accepted: 8 April 2022

Published: 12 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_56.png" /></figure>

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

## 1. Introduction

Food authentication is known to be a challenging task. The methodology applied depends on several factors, including sample type, type of adulteration, and the information required. Meat products are most commonly adulterated by the replacement of high-priced animal species by lower-quality or cheaper ones. Since DNA-based methodologies are highly suitable to detect genetic variations such as single nucleotide polymorphisms (SNPs), insertions, and deletions, they play a crucial role in the identification and differentiation of animal species in meat products [1–3]. DNA-based methodologies target either species-specific sequences in nuclear DNA or conserved regions in mitochondrial DNA [4]. Currently, authentication of meat products in official food laboratories is mainly based on real-time polymerase chain reaction (PCR) assays and/or DNA arrays. Multiplex real-time PCR assays, allowing the identification and quantification of multiple species in one and the same well, are particularly applicable for routine analysis because they allow saving time and costs. Multiplex real-time PCR assays have not only been developed for domesticated species, e.g., beef, pig, chicken, and turkey [5], and beef, pig, horse, and sheep [6], but also

<footnote n="1">¹ Austrian Agency for Health and Food Safety (AGES), Department for Molecular Biology and Microbiology, Institute for Food Safety Vienna, Spargelfeldstrasse 191, 1220 Vienna, Austria; stefanie.dobrovolny@ages.at</footnote>

<footnote n="2">² QuoData GmbH, Prellerstrasse 14, 01309 Dresden, Germany; steffen.uhlig@quodata.de (S.U.); kirstin.frost@quodata.de (K.F.); anja.schlierf@quodata.de (A.S.); kapil.nichani@quodata.de (K.N.); kirsten.simon@quodata.de (K.S.)</footnote>

<footnote n="3">³ Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 38, 1090 Vienna, Austria</footnote>

<footnote>* Correspondence: margit.cichna@univie.ac.at (M.C.-M.); rupert.hochegger@ages.at (R.H.)</footnote>
for game species, e.g., roe deer, red deer, fallow deer, and sika deer [7]. However, the low number of optical channels of real-time PCR instruments limits the number of species that can be targeted simultaneously.

Next generation sequencing (NGS) technologies, in particular massively parallel sequencing of PCR products based on the analysis of species-specific differences in DNA sequences (DNA barcoding), is being considered a promising alternative [8–10]. So-called DNA metabarcoding offers the possibility to identify a wide variety of species not only in individual foodstuffs but even in complex mixtures. Moreover, in contrast to real-time PCR assays, it is an untargeted approach, allowing the detection of species one has not been looking for.

We have recently developed a DNA metabarcoding method for 15 mammalian species and six poultry species, which are quite frequently contained in European foodstuff [11]. In order to detect both mammalian and poultry species, a primer pair for mammals and a primer pair for poultry species was combined in a duplex PCR assay. A ~120 bp fragment of the mitochondrial 16S ribosomal DNA gene serves as barcode region. The DNA metabarcoding method has been validated with regard to specificity, repeatability, robustness, accuracy, and limit of detection (LOD) [11]. In-house validation data showed that the DNA metabarcoding method can be used for routine applications. Meat species can be identified down to a concentration of 0.1%. Very recently, the applicability of the DNA metabarcoding method for routine analysis was further investigated by the analysis of a total of 104 samples (25 reference samples, 56 food products, and 23 pet food products). Results obtained by DNA metabarcoding were in line with those obtained by real-time PCR and/or a commercial DNA array [12]. However, interlaboratory evaluation of novel methods is a prerequisite for standardization and harmonization.

In this study, we summarized the results of an interlaboratory ring trial for the DNA metabarcoding method, initiated by the §64 German Food and Feed Code (LFGB) working group "NGS Species Identification", chaired by the Federal Office of Consumer Protection and Food Safety (BVL) in Germany. One goal of the working group is the validation and standardization of (screening) methods for the identification and differentiation of animal species based on next generation amplicon sequencing for food authentication. The interlaboratory ring trial was coordinated by the Austrian Agency for Health and Food Safety (AGES) in 2020 and involved 15 participating laboratories. The aim was to evaluate the performance (e.g., repeatability, reproducibility, accuracy) of the DNA metabarcoding method in detail.

## 2. Materials and Methods

### 2.1. *Participating Laboratories*

The interlaboratory ring trial was organized by the AGES on behalf of the BVL. The following laboratories participated in the ring trial (in alphabetical order): Bavarian State Office for Food Safety and Health (LGL), Oberschleißheim, Germany; Chemical and Veterinary Analytical Institute Muensterland-Emscher-Lippe (CVUA-MEL), Muenster, Germany; Chemical and Veterinary Investigation Office Freiburg (CVUA-FR), Freiburg, Germany; Chemical and Veterinary Investigation Office Karlsruhe (CVUA-KA), Karlsruhe, Germany; Eurofins Genomics Europe Applied Genomics GmbH, Ebersberg, Germany; StarSEQ GmbH, Mainz, Germany; Labor Kneissler GmbH and Co. KG, Burglengenfeld, Germany; Saxony-Anhalt State Office for Consumer Protection (LAV S-A), Halle, Germany; State Office Laboratory Hessen (LHL), Kassel, Germany; Max Rubner-Institut (MRI)/National Reference Centre for Authentic Food (NRZ-Authent), Kulmbach, Germany; Max Planck Institute for Plant Breeding Research (MPIPZ), Köln, Germany; Lower Saxony State Office for Consumer Protection and Food Safety (LAVES), Niedersachsen, Germany; AGES, Vienna, Austria; PLANTON Laboratory for Analysis and Biotechnology GmbH, Kiel, Germany; SGS Institute Fresenius GmbH, Freiburg, Germany.
## 2.2. Samples

In the course of the interlaboratory ring trial, eight samples had to be analyzed: six DNA extract mixtures (samples 1–6), one DNA extract from a model sausage (sample 7), and one DNA extract from maize (sample 8), serving as a negative control (Table 1).

<table><caption>**Table 1.** Sample composition. Samples 1–6: DNA extract mixtures; percentage refers to DNA (v/v). Sample 7: extract from a model sausage; percentage refers to meat content (w/w). Sample 8: DNA extract from maize (negative control). - indicates that the species is not in the DNA extract mixture/sample.</caption><thead><tr><th rowspan="2">Sample</th><th>Chicken</th><th>Horse</th><th>Turkey</th><th>Beef</th><th>Sheep</th><th>Pig</th><th>Goat</th></tr><tr><th colspan="7">Percentage (%)</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>94</td><td>1</td></tr><tr><td>2</td><td>-</td><td>1.9</td><td>0.5</td><td>65.7</td><td>1.9</td><td>30</td><td>-</td></tr><tr><td>3</td><td>1.9</td><td>66.1</td><td>1.9</td><td>-</td><td>0.5</td><td>-</td><td>30</td></tr><tr><td>4</td><td>-</td><td>0.5</td><td>-</td><td>30</td><td>67.5</td><td>0.1</td><td>1.9</td></tr><tr><td>5</td><td>67.5</td><td>-</td><td>-</td><td>1.9</td><td>30</td><td>0.5</td><td>0.1</td></tr><tr><td>6</td><td>0.1</td><td>30</td><td>67.5</td><td>0.5</td><td>-</td><td>1.9</td><td>-</td></tr><tr><td>7</td><td>5</td><td>-</td><td>5</td><td>50</td><td>-</td><td>40</td><td>-</td></tr><tr><td>8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

In total, seven animal species, including five mammalian species (*Sus scrofa domesticus* (pig), *Bos taurus* (cattle), *Equus caballus* (horse), *Ovis gmelini aries* (sheep), and *Capra aegagrus hircus* (goat)), and two poultry species (*Gallus gallus domesticus* (chicken) and *Meleagris gallopavo* (turkey)) were covered by the samples. All samples originated from muscle meat and were purchased from local meat suppliers.

Sample 1 contained DNA from seven animal species: DNA from pig as major component (94%, v/v), and DNA from six animal species (cattle, horse, sheep, goat, chicken, turkey; 1% (v/v) each). Samples 2–6 consisted of DNA from five animal species in varying proportions, ranging from 0.1% (v/v) to 67.5% (v/v). All DNA extract mixtures were prepared at the AGES.

The model sausage was produced according to the Codex Alimentarius Austriacus by the Higher Technical College for Food Technology Hollabrunn (Hollabrunn, Austria). The model sausage consisted of 50% (w/w) beef, 40% (w/w) pig, 5% (w/w) chicken, and 5% (w/w) turkey.

## 2.3. Genomic DNA Extraction

Extraction of genomic DNA from muscle meat of the seven animal species (pig, beef, horse, sheep, goat, chicken, turkey) was carried out at the CVUA-FR by applying the official method L 00.00–119 [13]. Identity of the animal species was verified by subjecting DNA extracts to Sanger sequencing and matching a ~464 base pair (bp) fragment of the mitochondrial cytochrome b gene against public databases provided by the National Center for Biotechnology Information (NCBI, Bethesda, MD, USA) [14,15]. After verification by Sanger sequencing and isolating genomic DNA fourfold, individual DNA extracts were combined. Total DNA of the (combined) DNA extracts was quantified by spectroscopy employing a UV/VIS spectrophotometer, adjusted to a DNA concentration of 20 ng/µL and sent to AGES. Isolation of DNA from the homogenized model sausage was performed at AGES [13].

The copy number of the mitochondrial 16S ribosomal DNA gene in the extracts from the respective animal species was determined by droplet digital PCR (ddPCR, QX200 Droplet Generator, QX200 Droplet Reader (Bio-Rad, Hercules, CA, USA)) using the EvaGreen Supermix. DNA extract mixtures were prepared at the AGES, by taking the copy numbers (pig (870 copies/µL), beef (1069 copies/µL), horse (1795 copies/µL), sheep (520 copies/µL), goat (790 copies/µL), chicken (620 copies/µL), turkey (673 copies/µL)),
into account. The percentages of samples 1 to 6 given in Table 1 were calculated by relating the DNA copy number of the respective animal species to the total number of copies of animal species in the sample.

## 2.4. Study Design

The interlaboratory ring trial for validation of the DNA metabarcoding method for mammalian and poultry species [11] was conducted in the framework of the §64 LFGB working group "NGS Species Identification" under the coordination of AGES. Statistical data analysis was performed by QuoData GmbH (Dresden, Germany).

For sequencing, three benchtop NGS instruments from two companies were used. Benchtop instruments from Illumina (Illumina, San Diego, CA, USA) were employed by eleven laboratories, whereof eight used the MiSeq instrument, three the iSeq 100 instrument, and one participant used both the MiSeq and the iSeq 100 instrument. Four laboratories applied the Ion GeneStudio S5 instrument from Thermo Fisher Scientific (Thermo Fisher Scientific, Waltham, MA, USA).

Each participant obtained 16 anonymously labelled samples, comprising two sub-samples of each of the eight samples (Table 1). Sixteen samples were chosen to allow the iSeq 100 platform to be included in the ring trial. This also enabled the use of the most cost-effective MiSeq Reagent Micro Kit v2 for a small number of samples on the Illumina platforms. Participants directly used all individual DNA extracts for DNA library preparation and subsequently for amplicon sequencing on a next-generation sequencing instrument. Together with the "ready-to-use" DNA extracts, the participants obtained reagents for creating DNA libraries, a sequencing kit, and a step-by-step instruction.

In order to be able to perform sequencing on the Ion GeneStudio S5 instrument (Thermo Fisher Scientific, Waltham, MA, USA), the protocol for preparation of DNA libraries and sequencing published previously by Dobrovolny et al. (2019) [11] had to be adapted as follows. Each of the two forward primers were elongated by a 3 bp barcode adapter, one of 16 different 10 bp barcodes (index sequence) and the overhang adapter sequence (A adapter). Each of the two reverse primers was linked to an overhang adapter sequence (trP1 adapter). The PCR setup did not include additional magnesium chloride solution. In the magnetic bead cleaning step, a total of 37.5 µL Agencourt® AMPure® XP beads (Beckman Coulter, Brea, CA, USA) was used and the DNA was eluted with 50 µL Tris-EDTA (TE) buffer. The average library size was 190 bp, and all DNA libraries were adjusted to 100 pM and were mixed together in a single 1.5 mL tube. A 25 pM DNA pool was used for sequencing. In general, any deviations from the protocols had to be reported by the participants.

Paired-end sequencing on an Illumina instrument was performed using either the MiSeq Reagent Micro Kit v2 (300-cycles) or the iSeq 100 i1 Reagent v2 (300-cycles), which included a 5% PhiX spike-in. The Ion Chef instrument was used with the Ion 510TM and Ion 520TM and Ion 530TM Kit-Chef and the Ion 520TM Chip Kit to perform template preparation, enrichment and chip loading. Finally, the sequencing reaction was started on the Ion GeneStudio S5 instrument.

To obtain information about the presence of the animal species in the samples, the sequencing output in FastQ format was processed with a multi-step analysis pipeline by using Galaxy (version 19.01) as described previously [12]. Before the resulting FastQ files were used as input for the data analysis, the raw binary base call (bcl) files generated by Illumina devices were converted to text files using the conversion software bcl2fastq2-v2.19.0.316 (Illumina, San Diego, CA, USA). The default demultiplexing option of one allowed mismatch in the barcode recognition of the Illumina software (-- barcode mismatches) was thereby set to zero (value: 0) and the step was also integrated into the pipeline. Preliminary tests had shown that this can increase the quality of index recognition. The Thermo Fisher instrument software uses this setting by default. The analysis pipeline for sequencing data of the Thermo Fisher Scientific (Waltham, MA, USA) platforms was modified because paired-end FastQ files do not exist in this case. Consequently, the
primer sequences were adapted according to the requirements of the analysis tool Cutadapt (Galaxy version 1.16.6 [16]) and the tool fastq-join (Galaxy version 1.1.2-806.1) [17] was removed. Dereplicated reads were directly matched against a customized database (AGES database) including 51 mitochondrial genomes from animals (Supplementary Table S1) and the public databases provided by NCBI using BLASTn [18]. The AGES database contains verified sequences from the NCBI database exclusively from food-relevant animal species. This reduces the time needed for alignment and is intended to avoid nonsense results. For each of the samples, results were listed automatically in a table according to taxonomy and abundance and a formula calculated the proportions of animal species by relating the number of reads for the respective species to the number of total reads (after pipeline) across all animal species obtained for the subsample. For further statistical analysis, all Excel spreadsheets were sent to QuoData GmbH.

## 2.5. Statistical Data Analysis

Statistical analyses were performed by QuoData GmbH. Even though the DNA metabarcoding method used in this interlaboratory comparison is commonly regarded as a qualitative method, the underlying decision process is based on the comparison of a quantitative value, namely the proportion of a single species, with a specific threshold. The performance of such a method can be assessed both on the basis of the qualitative result (yes/no) and on the basis of the underlying quantitative data. Because the information content of the quantitative data can be far greater than the corresponding qualitative data, the quantitative data were used in addition to the qualitative data to describe the performance of the DNA metabarcoding method.

In addition, the study of quantitative data also aimed to verify the extent to which this method can also be used for quantitative determinations.

### 2.5.1. Quantitative Statistical Analyses

Proportions of animal species ranged between 0.1% and 94%. To avoid asymmetric distributions for proportions near 0% and 100%, and to ensure equality of variances for the individual combinations of samples/animal species, the proportions were subjected to a logit transformation:

$$ \text{logit}(\text{proportion}) = \ln \left( \frac{\text{proportion}}{1 - \text{proportion}} \right) $$

The logit-transformed proportions can be retransformed as follows:

$$ \text{proportion} = \frac{e^{\text{logit}(\text{proportion})}}{1 + e^{\text{logit}(\text{proportion})}} $$

The logit-transformed proportions were then subjected to several outlier tests. Data were checked for systematic errors across samples and/or animal species affecting the mean values (Mandel statistics) and/or variances (Mandels statistics). In addition, the occurrence of sample- and animal species-specific outliers regarding the laboratory mean values and variances was tested for by means of the Grubbs and the Cochran tests (significance level 1%), respectively. Proportions identified as outliers were excluded from further statistical analyses.

Logit-transformed and outlier-cleaned data were checked for normal distribution using the Shapiro-Wilk test. Then, repeatability, reproducibility, and accuracy of the proportions of animal species were determined according to the criteria of QuoData certified with the aid of the software solution for method comparison studies and interlaboratory comparison studies PROLab Plus, version 2021.7.22.0 [19] (QuoData, Dresden, Germany), using the statistical methods according to DIN ISO 5725-2 and according to the specifications in the Official Collection of Test Methods ASU §64 LFGB for the statistical evaluation of ring trials for method validation [20]. Taking into account the obtained repeatability
and reproducibility standard deviations for samples 1–7, variance functions describing the
functional relationship between standard deviations and overall mean for the individual
combinations of samples/animal species were modelled.

For each of the combinations, the bias (difference) between this overall mean and the
proportion of the animal species added to the sample was also determined. Furthermore,
the standard deviation of this bias was calculated.

Prediction profiles and measurement uncertainty profiles were constructed, both not considering the bias (based on reproducibility standard deviations), and considering the bias (based on reproducibility standard deviations as well as on the standard deviation of the bias).

In addition, the z scores for each combination of lab/sample/animal species were
determined, providing a measure for the standardized deviations of laboratory mean values
from the respective overall mean value.

2.5.2. Qualitative Statistical Analyses

A sample was classified as false positive if for at least one animal species that had not been added, a proportion above a defined threshold was obtained. By contrast, a sample was considered false negative for a specific animal species if the proportion was below a defined threshold for this species.

The probability of detection for an arbitrary animal at a defined threshold for (1) a laboratory with average performance, (2) a laboratory with positive bias, and (3) a laboratory with negative bias was determined based on the variance functions by the quantitative statistical analysis.

3. Results and Discussion

Fourteen of the fifteen laboratories submitted their sequencing results in time. Ten laboratories applied the Illumina platform, with seven laboratories (01, 02, 03, 04, 06, 08, 14) using the MiSeq, two laboratories (07, 13) the iSeq 100, and one laboratory utilizing both the MiSeq and the iSeq 100 (referred to as "laboratory 15" and "laboratory 20", respectively). The remaining four laboratories (09, 10, 11, 12) applied the Ion GeneStudio S5 system from Thermo Fisher Scientific.

Each of the laboratories submitted 16 sequencing results in total (eight samples, two subsamples each), with the exception of laboratories 07, 12, and 13. Laboratory 07 did not provide the result for subsample 8B (negative control), whereas datasets submitted by laboratories 12 and 13 were lacking results for both subsamples of sample 8. With the exception of laboratory 03, sequencing was done by using the test kit provided by the AGES. The suitability of the MiSeq Reagent Kit v2 applied by laboratory 03 had been demonstrated in preliminary experiments.

FastQ data provided by the participating laboratories was evaluated by the AGES
by using the analysis pipeline in Galaxy. For identification of animal species, the DNA
sequences (reads) were aligned, once with the customized AGES database and once with
the NCBI database. Supplementary Table S2 summarizes the total number of reads for each
laboratory, taking into account the results obtained for each of the fourteen subsamples
containing animal species (samples 1–7).

Total numbers of raw reads that passed the analysis pipeline were quite different between laboratories. Very low total numbers of raw reads can, for example, be caused by errors during wet-lab activities, e.g., pipetting errors or error rate of DNA polymerase. In addition, problems with adapter- and index-recognition are known to have an impact. Loss of reads or their elimination by pipeline tools can also be caused by errors in PCR amplification of the library (e.g., index hopping), sequencing errors (e.g., inserts, substitutions, or deletions) or insufficient cluster resolution [21]. All these errors may affect the quality of raw data (FastQ file) and thus the number of DNA sequences (reads) after analysis pipeline.

Total numbers of reads obtained with the Ion GeneStudio S5 were significantly higher
than those obtained with the MiSeq (p < 0.001) and the iSeq 100 (p < 0.001). Differences ob-
served between the Illumina and the Thermo Fisher technology are caused by differences in data filtering. The instrument-specific software of the Ion GeneStudio S5 removes datasets of lower quality by filtering before starting the analysis pipeline. Thus, considerably more sequences remain after primary data analysis compared to the instruments from Illumina.

Differences in recoveries, by relating the total number of reads to the number of raw reads before analysis pipeline, between laboratories using the same instrument type (MiSeq, iSeq 100, or Ion GeneStudio S5, Table S3) hint at differences in the quality of the sequencing run and unintended loss of reads. Significant differences (*p* < 0.001) in recoveries between laboratories using Illumina instruments and those applying the Ion GeneStudio S5 were, however, expected. These differences are caused by the fact that the pipeline of the Ion GeneStudio S5 neither included paired-end sequencing nor a "joining step" as was the case with the Illumina platforms.

### 3.1. Quantitative Evaluation of Ring Trial Data

The aim of quantitative evaluation of ring trial data was to determine average proportions of the animal species that had been added to samples 1–7, and to identify resulting error components within and between laboratories.

#### 3.1.1. Proportions of Animal Species in Samples 1–7

Proportions of animal species were calculated by relating the number of reads for the respective species to the number of total reads (after pipeline) across all animal species obtained for the sample. Table 2 gives the proportions of animal species determined for sample 1 containing seven animal species (Table 2a), sample 2 (as a representative of samples consisting of five animal species; Table 2b) and sample 7, a model food sample (Table 2c). Results for samples 3, 4, 5, and 6 are shown as stacked bar plots (Figure 1).

Preliminary evaluation of the results indicated that the proportions of animal species determined considerably depended on the sequencing platform/technology applied. Due to the low number of laboratories using the sequencing technology from Thermo Fisher Scientific, only results obtained by laboratories applying Illumina platforms were included into statistical evaluation. Results obtained by laboratories 09–12 using the Ion GeneStudio S5 are only shown for comparison.

#### 3.1.2. Logit Transformation

Proportions of animal species in samples 1–7 were quite different, ranging from 0.1% to 94%. However, a prerequisite for the evaluation of ring trial data according to ASU §64 LFGB is that the proportions of animal species follow normal or at least symmetric distribution. In order to allow assumption of normal distribution and ensure equality of variances of the individual combinations of samples/animal species (after elimination of outliers), proportions of animal species were subjected to a logit transformation. The logit is the logarithm of the proportion of the animal species divided by 1 minus the proportion of the animal species. Proportions of, e.g., 0.1%, 1%, 10%, 30%, 50%, and 70%, resulted in logit values of −6.91, −4.60, −2.20, −0.85, 0, and 0.85, respectively. Since the logit for 0% and 100% is not defined, it was set to surrogate values of −15 and +15, respectively.

#### 3.1.3. Outlier Tests

In the course of evaluating ring trial data according to ASU §64 LFGB, logit-transformed proportions of animal species were subjected to several outlier tests (see also Section 2.5.1). Table 3 summarizes the outliers and reasons for their elimination.
<table><caption>Table 2. Proportion of animal species determined for samples 1 (a), 2 (b), and 7 (c). A and B refer to subsamples A and B, respectively.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th colspan="10">(a). Sample 1</th></tr><tr><th colspan="2">AGES Database</th><th colspan="2">NCBI Database</th><th colspan="2">Laboratory</th><th colspan="2">AGES Database</th><th colspan="2">NCBI Database</th></tr><tr><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th></th><th>A</th><th>B</th><th>A</th><th>B</th></tr></thead><tbody><tr><td>Pig</td><td>94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>91.25</td><td>91.77</td><td>91.21</td><td>91.74</td><td></td><td>95.58</td><td>94.83</td><td>95.56</td><td>94.81</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.42</td><td>1.37</td><td>1.42</td><td>1.37</td><td></td><td>0.73</td><td>1.08</td><td>0.73</td><td>1.08</td></tr><tr><td>Turkey</td><td>1</td><td></td><td>1.42</td><td>1.34</td><td>1.42</td><td>1.33</td><td></td><td>0.60</td><td>0.67</td><td>0.60</td><td>0.66</td></tr><tr><td>Beef</td><td>1</td><td>01</td><td>1.17</td><td>1.03</td><td>1.16</td><td>1.02</td><td>10</td><td>0.49</td><td>0.66</td><td>0.41</td><td>0.56</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.55</td><td>1.51</td><td>1.55</td><td>1.51</td><td></td><td>0.57</td><td>0.58</td><td>0.57</td><td>0.58</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.65</td><td>1.57</td><td>1.62</td><td>1.56</td><td></td><td>1.05</td><td>1.15</td><td>1.05</td><td>1.15</td></tr><tr><td></td><td>1</td><td></td><td>1.54</td><td>1.42</td><td>1.56</td><td>1.44</td><td></td><td>0.97</td><td>1.03</td><td>0.98</td><td>1.04</td></tr><tr><td>Pig</td><td>94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>90.31</td><td>90.33</td><td>90.29</td><td>90.30</td><td></td><td>94.62</td><td>94.90</td><td>94.59</td><td>94.87</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.50</td><td>1.57</td><td>1.50</td><td>1.57</td><td></td><td>0.47</td><td>0.30</td><td>0.47</td><td>0.30</td></tr><tr><td>Turkey</td><td>1</td><td>02</td><td>1.64</td><td>1.59</td><td>1.64</td><td>1.59</td><td></td><td>1.01</td><td>1.09</td><td>1.00</td><td>1.09</td></tr><tr><td>Beef</td><td>1</td><td></td><td>1.32</td><td>1.41</td><td>1.31</td><td>1.41</td><td>11</td><td>0.54</td><td>0.43</td><td>0.51</td><td>0.40</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.77</td><td>1.67</td><td>1.77</td><td>1.67</td><td></td><td>1.02</td><td>1.03</td><td>1.02</td><td>1.03</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.81</td><td>1.73</td><td>1.79</td><td>1.72</td><td></td><td>1.19</td><td>1.16</td><td>1.19</td><td>1.16</td></tr><tr><td></td><td>1</td><td></td><td>1.66</td><td>1.69</td><td>1.68</td><td>1.71</td><td></td><td>1.15</td><td>1.10</td><td>1.16</td><td>1.11</td></tr><tr><td>Pig</td><td>94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>92.67</td><td>92.48</td><td>92.65</td><td>92.46</td><td></td><td>94.30</td><td>94.68</td><td>94.27</td><td>94.65</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.04</td><td>1.11</td><td>1.04</td><td>1.11</td><td></td><td>0.56</td><td>0.36</td><td>0.57</td><td>0.36</td></tr><tr><td>Turkey</td><td>1</td><td></td><td>1.04</td><td>1.14</td><td>1.04</td><td>1.14</td><td></td><td>1.10</td><td>1.13</td><td>1.10</td><td>1.13</td></tr><tr><td>Beef</td><td>1</td><td>03</td><td>0.90</td><td>0.90</td><td>0.89</td><td>0.89</td><td>12</td><td>0.66</td><td>0.48</td><td>0.64</td><td>0.47</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.60</td><td>1.55</td><td>1.60</td><td>1.55</td><td></td><td>0.98</td><td>1.03</td><td>0.98</td><td>1.04</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.44</td><td>1.48</td><td>1.43</td><td>1.46</td><td></td><td>1.24</td><td>1.19</td><td>1.24</td><td>1.19</td></tr><tr><td></td><td>1</td><td></td><td>1.31</td><td>1.34</td><td>1.32</td><td>1.35</td><td></td><td>1.16</td><td>1.13</td><td>1.16</td><td>1.14</td></tr><tr><td>Pig</td><td>94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>92.08</td><td>92.28</td><td>92.06</td><td>92.25</td><td></td><td>92.01</td><td>91.80</td><td>91.98</td><td>91.77</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.16</td><td>1.21</td><td>1.16</td><td>1.21</td><td></td><td>1.27</td><td>1.33</td><td>1.27</td><td>1.33</td></tr><tr><td>Turkey</td><td>1</td><td>04</td><td>1.22</td><td>1.14</td><td>1.22</td><td>1.14</td><td></td><td>1.35</td><td>1.38</td><td>1.35</td><td>1.38</td></tr><tr><td>Beef</td><td>1</td><td></td><td>1.09</td><td>1.00</td><td>1.09</td><td>0.99</td><td>13</td><td>1.22</td><td>1.17</td><td>1.22</td><td>1.16</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.56</td><td>1.56</td><td>1.56</td><td>1.56</td><td></td><td>1.45</td><td>1.52</td><td>1.45</td><td>1.52</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.45</td><td>1.49</td><td>1.45</td><td>1.48</td><td></td><td>1.38</td><td>1.37</td><td>1.38</td><td>1.37</td></tr><tr><td></td><td>1</td><td></td><td>1.43</td><td>1.33</td><td>1.44</td><td>1.34</td><td></td><td>1.32</td><td>1.43</td><td>1.34</td><td>1.44</td></tr></tbody></table>
<table><caption>Table 2. Cont.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th colspan="10">(a). Sample 1</th></tr><tr><th colspan="2">AGES Database</th><th colspan="2">NCBI Database</th><th colspan="2">AGES Database</th><th colspan="2">NCBI Database</th></tr><tr><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th></tr></thead><tbody><tr><td>Pig</td><td>94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>90.36</td><td>90.41</td><td>90.33</td><td>90.38</td><td>90.44</td><td>93.48</td><td>90.41</td><td>93.46</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.51</td><td>1.53</td><td>1.51</td><td>1.53</td><td>1.57</td><td>0.97</td><td>1.57</td><td>0.97</td></tr><tr><td>Turkey</td><td>1</td><td></td><td>1.52</td><td>1.56</td><td>1.51</td><td>1.56</td><td>1.32</td><td>0.92</td><td>1.32</td><td>0.92</td></tr><tr><td>Beef</td><td>1</td><td>06</td><td>1.36</td><td>1.33</td><td>1.36</td><td>1.32</td><td>1.14</td><td>0.82</td><td>1.13</td><td>0.82</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.87</td><td>1.84</td><td>1.87</td><td>1.84</td><td>2.17</td><td>1.50</td><td>2.17</td><td>1.50</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.72</td><td>1.67</td><td>1.71</td><td>1.66</td><td>1.81</td><td>1.24</td><td>1.80</td><td>1.24</td></tr><tr><td>Pig</td><td>94</td><td></td><td>92.48</td><td>92.19</td><td>92.47</td><td>92.17</td><td>92.22</td><td>92.23</td><td>92.18</td><td>92.20</td></tr><tr><td>Chicken</td><td>1</td><td></td><td>1.08</td><td>1.17</td><td>1.08</td><td>1.17</td><td>1.19</td><td>1.18</td><td>1.19</td><td>1.18</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.21</td><td>1.27</td><td>1.21</td><td>1.27</td><td>1.14</td><td>1.21</td><td>1.14</td><td>1.21</td></tr><tr><td>Turkey</td><td>1</td><td>07</td><td>1.03</td><td>1.10</td><td>1.02</td><td>1.10</td><td>0.98</td><td>0.96</td><td>0.98</td><td>0.96</td></tr><tr><td>Beef</td><td>1</td><td></td><td>1.51</td><td>1.50</td><td>1.51</td><td>1.50</td><td>1.60</td><td>1.55</td><td>1.60</td><td>1.55</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.36</td><td>1.38</td><td>1.37</td><td>1.38</td><td>1.48</td><td>1.45</td><td>1.47</td><td>1.44</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.32</td><td>1.39</td><td>1.32</td><td>1.40</td><td>1.39</td><td>1.41</td><td>1.41</td><td>1.42</td></tr><tr><td>Pig</td><td>94</td><td></td><td>91.31</td><td>91.28</td><td>91.28</td><td>91.25</td><td>92.59</td><td>92.63</td><td>92.58</td><td>92.61</td></tr><tr><td>Chicken</td><td>1</td><td></td><td>1.34</td><td>1.40</td><td>1.34</td><td>1.40</td><td>1.03</td><td>1.09</td><td>1.03</td><td>1.09</td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.40</td><td>1.32</td><td>1.40</td><td>1.32</td><td>1.24</td><td>1.28</td><td>1.24</td><td>1.28</td></tr><tr><td>Turkey</td><td>1</td><td>08</td><td>1.28</td><td>1.23</td><td>1.27</td><td>1.23</td><td>20</td><td>1.03</td><td>1.11</td><td>1.02</td></tr><tr><td>Beef</td><td>1</td><td></td><td>1.68</td><td>1.62</td><td>1.68</td><td>1.62</td><td>1.44</td><td>1.39</td><td>1.44</td><td>1.39</td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.53</td><td>1.59</td><td>1.52</td><td>1.57</td><td>1.31</td><td>1.30</td><td>1.31</td><td>1.29</td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.46</td><td>1.56</td><td>1.48</td><td>1.58</td><td>1.27</td><td>1.28</td><td>1.28</td><td>1.29</td></tr><tr><td>Pig</td><td>94</td><td></td><td>94.62</td><td>93.61</td><td>94.60</td><td>93.59</td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>1</td><td></td><td>0.64</td><td>1.07</td><td>0.64</td><td>1.08</td><td></td><td></td><td></td><td></td></tr><tr><td>Horse</td><td>1</td><td></td><td>1.06</td><td>1.19</td><td>1.06</td><td>1.19</td><td></td><td></td><td></td><td></td></tr><tr><td>Turkey</td><td>1</td><td>09</td><td>0.63</td><td>1.10</td><td>0.61</td><td>1.07</td><td></td><td></td><td></td><td></td></tr><tr><td>Beef</td><td>1</td><td></td><td>0.92</td><td>0.90</td><td>0.92</td><td>0.90</td><td></td><td></td><td></td><td></td></tr><tr><td>Sheep</td><td>1</td><td></td><td>1.10</td><td>1.05</td><td>1.10</td><td>1.05</td><td></td><td></td><td></td><td></td></tr><tr><td>Goat</td><td>1</td><td></td><td>1.04</td><td>1.07</td><td>1.05</td><td>1.08</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 2. Cont.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th rowspan="3">Laboratory</th><th colspan="6">(b). Sample 2</th><th colspan="2">NCBI Database</th></tr><tr><th colspan="2">AGES Database</th><th colspan="2">NCBI Database</th><th rowspan="2">Laboratory</th><th colspan="2">AGES Database</th></tr><tr><th>A</th><th>B</th><th>A</th><th>B</th><th>A</th><th>B</th><th>A</th><th>B</th></tr></thead><tbody><tr><td>Beef</td><td>65.7</td><td></td><td>64.59</td><td>65.91</td><td>64.42</td><td>65.73</td><td>53.26</td><td>55.43</td><td>53.14</td><td>55.32</td></tr><tr><td>Pig</td><td>30</td><td></td><td>30.04</td><td>29.36</td><td>29.93</td><td>29.24</td><td>41.44</td><td>40.09</td><td>41.25</td><td>39.91</td></tr><tr><td>Horse</td><td>1.9</td><td>01</td><td>2.24</td><td>1.97</td><td>2.23</td><td>1.96</td><td>10</td><td>1.63</td><td>2.19</td><td>1.61</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.61</td><td>2.33</td><td>2.58</td><td>2.30</td><td></td><td>2.71</td><td>2.88</td><td>2.70</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.46</td><td>0.40</td><td>0.45</td><td>0.39</td><td></td><td>0.13</td><td>0.16</td><td>0.11</td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>62.99</td><td>63.18</td><td>62.83</td><td>63.01</td><td>66.01</td><td>65.78</td><td>65.89</td><td>65.68</td></tr><tr><td>Pig</td><td>30</td><td></td><td>31.30</td><td>31.24</td><td>31.20</td><td>31.12</td><td></td><td>29.52</td><td>29.75</td><td>29.40</td></tr><tr><td>Horse</td><td>1.9</td><td>02</td><td>2.49</td><td>2.44</td><td>2.48</td><td>2.43</td><td>11</td><td>2.03</td><td>2.00</td><td>2.02</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.60</td><td>2.56</td><td>2.57</td><td>2.53</td><td></td><td>2.16</td><td>2.27</td><td>2.15</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.61</td><td>0.54</td><td>0.60</td><td>0.53</td><td></td><td>0.28</td><td>0.20</td><td>0.26</td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>71.67</td><td>70.55</td><td>71.48</td><td>70.36</td><td>66.29</td><td>64.97</td><td>66.22</td><td>64.88</td></tr><tr><td>Pig</td><td>30</td><td></td><td>24.75</td><td>25.50</td><td>24.68</td><td>25.43</td><td></td><td>29.13</td><td>30.25</td><td>28.98</td></tr><tr><td>Horse</td><td>1.9</td><td>03</td><td>1.37</td><td>1.44</td><td>1.36</td><td>1.44</td><td>12</td><td>2.08</td><td>2.23</td><td>2.06</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>1.90</td><td>2.16</td><td>1.88</td><td>2.13</td><td></td><td>2.23</td><td>2.31</td><td>2.22</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.30</td><td>0.33</td><td>0.29</td><td>0.33</td><td></td><td>0.27</td><td>0.24</td><td>0.25</td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>67.22</td><td>67.55</td><td>67.05</td><td>67.39</td><td>62.21</td><td>61.73</td><td>62.00</td><td>61.52</td></tr><tr><td>Pig</td><td>30</td><td></td><td>28.26</td><td>28.10</td><td>28.17</td><td>28.01</td><td></td><td>32.37</td><td>32.75</td><td>32.26</td></tr><tr><td>Horse</td><td>1.9</td><td>04</td><td>1.90</td><td>1.76</td><td>1.89</td><td>1.75</td><td>13</td><td>2.52</td><td>2.55</td><td>2.51</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.18</td><td>2.16</td><td>2.17</td><td>2.14</td><td></td><td>2.31</td><td>2.31</td><td>2.30</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.40</td><td>0.37</td><td>0.39</td><td>0.37</td><td></td><td>0.56</td><td>0.62</td><td>0.55</td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>66.46</td><td>65.45</td><td>66.28</td><td>65.24</td><td>70.20</td><td>68.65</td><td>69.99</td><td>68.47</td></tr><tr><td>Pig</td><td>30</td><td></td><td>28.63</td><td>29.35</td><td>28.54</td><td>29.24</td><td></td><td>26.07</td><td>27.18</td><td>25.99</td></tr><tr><td>Horse</td><td>1.9</td><td>06</td><td>2.09</td><td>2.10</td><td>2.08</td><td>2.09</td><td>14</td><td>1.44</td><td>1.63</td><td>1.43</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.32</td><td>2.54</td><td>2.31</td><td>2.52</td><td></td><td>1.94</td><td>2.15</td><td>1.92</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.47</td><td>0.52</td><td>0.47</td><td>0.52</td><td></td><td>0.32</td><td>0.36</td><td>0.32</td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>65.64</td><td>66.96</td><td>65.46</td><td>66.77</td><td>69.77</td><td>67.63</td><td>69.61</td><td>67.48</td></tr><tr><td>Pig</td><td>30</td><td></td><td>29.78</td><td>28.89</td><td>29.68</td><td>28.80</td><td></td><td>26.43</td><td>27.98</td><td>26.36</td></tr><tr><td>Horse</td><td>1.9</td><td>07</td><td>1.93</td><td>1.73</td><td>1.92</td><td>1.72</td><td>15</td><td>1.49</td><td>1.77</td><td>1.48</td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.17</td><td>2.04</td><td>2.17</td><td>2.03</td><td></td><td>1.96</td><td>2.20</td><td>1.93</td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.47</td><td>0.37</td><td>0.47</td><td>0.37</td><td></td><td>0.31</td><td>0.37</td><td>0.31</td></tr></tbody></table>
<table><caption>Table 2. Cont.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th colspan="6">(b). Sample 2</th><th colspan="6">(c). Sample 7</th></tr><tr><th colspan="3">AGES Database</th><th colspan="3">NCBI Database</th><th colspan="3">AGES Database</th><th colspan="3">NCBI Database</th></tr><tr><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th>A</th><th>B</th></tr></thead><tbody><tr><td>Beef</td><td>65.7</td><td></td><td>68.90</td><td>69.26</td><td>68.70</td><td>69.06</td><td></td><td>68.01</td><td>65.33</td><td>67.81</td><td>65.14</td><td></td></tr><tr><td>Pig</td><td>30</td><td></td><td>26.45</td><td>26.12</td><td>26.36</td><td>26.03</td><td></td><td>28.10</td><td>30.10</td><td>28.02</td><td>30.01</td><td></td></tr><tr><td>Horse</td><td>1.9</td><td>08</td><td>1.92</td><td>1.85</td><td>1.91</td><td>1.84</td><td>20</td><td>1.69</td><td>2.06</td><td>1.69</td><td>2.05</td><td></td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.26</td><td>2.31</td><td>2.23</td><td>2.28</td><td></td><td>1.85</td><td>2.09</td><td>1.84</td><td>2.08</td><td></td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.43</td><td>0.43</td><td>0.43</td><td>0.43</td><td></td><td>0.34</td><td>0.42</td><td>0.34</td><td>0.42</td><td></td></tr><tr><td>Beef</td><td>65.7</td><td></td><td>63.64</td><td>63.11</td><td>63.64</td><td>63.11</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pig</td><td>30</td><td></td><td>31.59</td><td>32.21</td><td>31.38</td><td>31.99</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Horse</td><td>1.9</td><td>09</td><td>2.26</td><td>2.21</td><td>2.24</td><td>2.18</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sheep</td><td>1.9</td><td></td><td>2.26</td><td>2.25</td><td>2.24</td><td>2.23</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Turkey</td><td>0.5</td><td></td><td>0.24</td><td>0.22</td><td>0.23</td><td>0.21</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th colspan="6">AGES Database</th><th colspan="6">NCBI Database</th></tr><tr><th colspan="3">AGES Database</th><th colspan="3">NCBI Database</th><th colspan="3">AGES Database</th><th colspan="3">NCBI Database</th></tr><tr><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th>Laboratory</th><th>A</th><th>B</th><th>A</th><th>B</th><th>A</th><th>B</th></tr><tr><td>Beef</td><td>50</td><td></td><td>45.79</td><td>45.80</td><td>45.75</td><td>45.78</td><td></td><td>34.33</td><td>34.58</td><td>34.34</td><td>34.59</td><td></td></tr><tr><td>Pig</td><td>40</td><td></td><td>42.31</td><td>42.71</td><td>42.25</td><td>42.64</td><td></td><td>61.16</td><td>60.60</td><td>61.03</td><td>60.47</td><td></td></tr><tr><td>Chicken</td><td>5</td><td>01</td><td>7.56</td><td>7.35</td><td>7.55</td><td>7.35</td><td>10</td><td>3.32</td><td>3.46</td><td>3.32</td><td>3.45</td><td></td></tr><tr><td>Turkey</td><td>5</td><td></td><td>4.31</td><td>4.08</td><td>4.28</td><td>4.05</td><td></td><td>1.16</td><td>1.33</td><td>0.95</td><td>1.13</td><td></td></tr><tr><td>Beef</td><td>50</td><td></td><td>48.54</td><td>48.18</td><td>48.50</td><td>48.16</td><td></td><td>45.20</td><td>43.03</td><td>45.19</td><td>43.03</td><td></td></tr><tr><td>Pig</td><td>40</td><td></td><td>40.68</td><td>40.46</td><td>40.62</td><td>40.40</td><td></td><td>50.41</td><td>45.73</td><td>50.30</td><td>45.63</td><td></td></tr><tr><td>Chicken</td><td>5</td><td>02</td><td>6.71</td><td>6.77</td><td>6.70</td><td>6.76</td><td>11</td><td>2.52</td><td>6.59</td><td>2.52</td><td>6.59</td><td></td></tr><tr><td>Turkey</td><td>5</td><td></td><td>3.99</td><td>4.26</td><td>3.97</td><td>4.25</td><td></td><td>1.81</td><td>4.60</td><td>1.70</td><td>4.35</td><td></td></tr><tr><td>Beef</td><td>50</td><td></td><td>52.15</td><td>52.30</td><td>52.08</td><td>52.24</td><td></td><td>45.87</td><td>42.72</td><td>45.91</td><td>42.76</td><td></td></tr><tr><td>Pig</td><td>40</td><td></td><td>38.50</td><td>38.68</td><td>38.45</td><td>38.64</td><td></td><td>49.65</td><td>44.84</td><td>49.50</td><td>44.72</td><td></td></tr><tr><td>Chicken</td><td>5</td><td>03</td><td>6.10</td><td>5.87</td><td>6.09</td><td>5.86</td><td>12</td><td>2.58</td><td>7.14</td><td>2.58</td><td>7.13</td><td></td></tr><tr><td>Turkey</td><td>5</td><td></td><td>3.24</td><td>3.13</td><td>3.20</td><td>3.10</td><td></td><td>1.84</td><td>5.24</td><td>1.78</td><td>5.08</td><td></td></tr><tr><td>Beef</td><td>50</td><td></td><td>49.31</td><td>49.59</td><td>49.29</td><td>49.57</td><td></td><td>44.13</td><td>43.46</td><td>44.08</td><td>43.41</td><td></td></tr><tr><td>Pig</td><td>40</td><td></td><td>40.47</td><td>40.16</td><td>40.42</td><td>40.11</td><td></td><td>43.46</td><td>44.09</td><td>43.40</td><td>44.03</td><td></td></tr><tr><td>Chicken</td><td>5</td><td>04</td><td>6.23</td><td>6.21</td><td>6.22</td><td>6.20</td><td>13</td><td>7.62</td><td>7.44</td><td>7.61</td><td>7.43</td><td></td></tr><tr><td>Turkey</td><td>5</td><td></td><td>3.70</td><td>3.77</td><td>3.69</td><td>3.76</td><td></td><td>4.63</td><td>4.85</td><td>4.62</td><td>4.84</td><td></td></tr></tbody></table>
<table><caption>Table 2. Cont.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th rowspan="3">Laboratory</th><th colspan="6">(c). Sample 7</th></tr><tr><th colspan="3">AGES Database</th><th colspan="2">NCBI Database</th><th colspan="2">NCBI Database</th></tr><tr><th>A</th><th>B</th><th>A</th><th>B</th><th>Laboratory</th><th>A</th><th>B</th></tr></thead><tbody><tr><td>Beef</td><td>50</td><td></td><td>50.05</td><td>49.52</td><td>50.01</td><td>49.46</td><td>50.95</td><td>51.46</td><td>50.90</td><td>51.40</td></tr><tr><td>Pig</td><td>40</td><td></td><td>38.40</td><td>39.33</td><td>38.35</td><td>39.27</td><td>38.49</td><td>38.71</td><td>38.44</td><td>38.66</td></tr><tr><td>Chicken</td><td>5</td><td>06</td><td>7.42</td><td>7.12</td><td>7.41</td><td>7.11</td><td>6.75</td><td>6.22</td><td>6.74</td><td>6.22</td></tr><tr><td>Turkey</td><td>5</td><td></td><td>4.08</td><td>4.00</td><td></td><td>4.07</td><td>3.78</td><td>3.59</td><td>3.76</td><td>3.58</td></tr><tr><td>Beef</td><td>50</td><td></td><td>47.44</td><td>48.52</td><td>47.39</td><td>48.47</td><td>49.53</td><td>48.78</td><td>49.49</td><td>48.74</td></tr><tr><td>Pig</td><td>40</td><td></td><td>42.22</td><td>41.83</td><td>42.18</td><td>41.78</td><td>39.38</td><td>39.84</td><td>39.34</td><td>39.80</td></tr><tr><td>Chicken</td><td>5</td><td>07</td><td>6.21</td><td>5.89</td><td>6.20</td><td>5.88</td><td>7.03</td><td>7.24</td><td>7.02</td><td>7.24</td></tr><tr><td>Turkey</td><td>5</td><td></td><td>4.09</td><td>3.72</td><td>4.08</td><td>3.72</td><td>4.02</td><td>4.13</td><td>4.00</td><td>4.11</td></tr><tr><td>Beef</td><td>50</td><td></td><td>51.82</td><td>51.73</td><td>51.75</td><td>51.67</td><td>46.99</td><td>46.15</td><td>46.94</td><td>46.11</td></tr><tr><td>Pig</td><td>40</td><td></td><td>35.16</td><td>36.23</td><td>35.11</td><td>36.18</td><td>41.48</td><td>42.03</td><td>41.45</td><td>42.00</td></tr><tr><td>Chicken</td><td>5</td><td>08</td><td>8.61</td><td>7.82</td><td>8.59</td><td>7.81</td><td>6.84</td><td>7.18</td><td>6.84</td><td>7.18</td></tr><tr><td>Turkey</td><td>5</td><td></td><td>4.38</td><td>4.18</td><td>4.37</td><td>4.16</td><td>4.66</td><td>4.61</td><td>4.65</td><td>4.60</td></tr><tr><td>Beef</td><td>50</td><td></td><td>44.59</td><td>43.04</td><td>44.70</td><td>43.14</td><td></td><td></td><td></td><td></td></tr><tr><td>Pig</td><td>40</td><td></td><td>50.65</td><td>51.23</td><td>50.45</td><td>51.03</td><td></td><td></td><td></td><td></td></tr><tr><td>Chicken</td><td>5</td><td>09</td><td>2.87</td><td>3.47</td><td>2.86</td><td>3.46</td><td></td><td></td><td></td><td></td></tr><tr><td>Turkey</td><td>5</td><td></td><td>1.87</td><td>2.22</td><td>1.80</td><td>2.15</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_57.png" /><figcaption>**Figure 1.** Proportions of animal species determined for samples 3, 4, 5, and 6. (A) AGES database, (B) NCBI database. Laboratories 01–06, 08, 14, 15: MiSeq; laboratories 07, 13, 20: iSeq 100; laboratories 09–12: Ion GeneStudio S5.</figcaption></figure>

<table><caption>**Table 3.** Summary of eliminated outliers.</caption><thead><tr><th>Sample</th><th>Species</th><th>Laboratory</th><th>Reason</th></tr></thead><tbody><tr><td>1</td><td>All species (n = 7)</td><td>14</td><td>Excessive variance of results for both subsamples</td></tr><tr><td rowspan="2">3</td><td rowspan="2">Sheep</td><td>06</td><td>Too high laboratory mean value</td></tr><tr><td>15</td><td>Too high laboratory mean value</td></tr><tr><td rowspan="2">4</td><td>Pig</td><td>02</td><td>Excessive variance of results for both subsamples</td></tr><tr><td>Chicken</td><td>08</td><td>Too high laboratory mean value</td></tr><tr><td rowspan="3">5</td><td rowspan="3">Sheep</td><td>13</td><td>Too high laboratory mean value</td></tr><tr><td>08</td><td>Too low laboratory mean value</td></tr><tr><td>13</td><td>Too low laboratory mean value</td></tr><tr><td>6</td><td>Chicken</td><td>15</td><td>Excessive variance of results for both subsamples</td></tr></tbody></table>

In case an outlier was only identified for one database (either AGES or NCBI database), it was, however, eliminated for both databases to ensure data comparability. In total, 15 of 396 (3.8%) combinations of laboratory/sample/animal species were identified as outliers and excluded from further evaluation for each of the databases.
### 3.1.4. Distribution of Sample-Specific Proportions of Animal Species

After outlier elimination, sample-specific logit-transformed proportions of animal species were tested for normal distribution by Kernel density estimation and the Shapiro-Wilk test.

A small number of cases were found to have a bimodal distribution. However, the Shapiro-Wilk test did not show evidence for non-normality for any of the combinations of samples/animal species. Thus, logit-transformed proportions of animal species could be subjected to further statistical evaluation.

### 3.1.5. Statistical Parameters According to ASU §64 LFGB

Logit-transformed and outlier-cleaned data were normally distributed and thus could be subjected to statistical evaluation according to ASU §64 LFGB. Table 4 gives—for each animal species and both databases (AGES and NCBI)—main statistical parameters, including re-transformed mean value, reproducibility standard deviation ($s_R$), and repeatability standard deviation ($s_r$). For the sake of completeness, the logit-transformed parameters are shown as well. The reproducibility standard deviation characterizes the variability of results between laboratories, and the repeatability standard deviation the variability within a laboratory under constant conditions, i.e., the variability of results obtained for the two subsamples of the same sample.

### 3.1.6. Dependence of Bias, Reproducibility Standard Deviation, and Repeatability Standard Deviation on the Mean Proportion of Animal Species

Next, it was evaluated whether the bias between the proportion of the animal species added to the sample and the overall mean determined in the ring trial, as well as whether reproducibility standard deviation and repeatability standard deviation depended on the proportion of the respective animal species (Figure 2A) and/or the predominant animal species in the sample (Figure 2B). Evaluation was based on the statistical parameters for logit-transformed proportions of animal species.

<figure><img src="image_58.png" /></figure>

(A)

<figure><img src="image_59.png" /></figure>

(B)

**Figure 2.** Bias, reproducibility standard deviation, and repeatability standard deviation depending on the overall mean determined in the ring trial based on logit-transformed proportions of animal species. Colors refer to (A) the respective animal species and (B) the predominant animal species in the respective sample.
<table><caption>Table 4. Statistical parameters for relative proportions of animal species according to ASU §64 LFGB. AGES; AGES database, NCBI: NCBI database.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Parameter <sup>1</sup></th><th colspan="12">Sample</th></tr><tr><th colspan="2">1</th><th colspan="2">2</th><th colspan="2">3</th><th colspan="2">4</th><th colspan="2">5</th><th colspan="2">6</th><th colspan="2">7</th></tr><tr><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th></tr></thead><tbody><tr><td rowspan="7">Chicken</td><td>Number of labs</td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td></tr><tr><td>Number of labs after<br>outlier elimination</td><td>10</td><td>10</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>9</td><td>9</td><td></td><td></td><td>10</td><td>10</td></tr><tr><td>Proportion (v/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td>1.27%</td><td>1.26%</td><td></td><td></td><td>2.12%</td><td>2.11%</td><td></td><td></td><td>55.49%</td><td>55.34%</td><td></td><td></td><td>0.15%</td><td>6.89%</td><td>5%</td></tr><tr><td>S<sub>R</sub></td><td>0.18%</td><td>0.18%</td><td></td><td></td><td>0.23%</td><td>0.23%</td><td></td><td></td><td>0.84%</td><td>0.84%</td><td></td><td></td><td>0.03%</td><td>0.72%</td><td>0.71%</td></tr><tr><td>S<sub>r</sub></td><td>0.04%</td><td>0.04%</td><td></td><td></td><td>0.08%</td><td>0.08%</td><td></td><td></td><td>0.57%</td><td>0.57%</td><td></td><td></td><td>0.01%</td><td>0.24%</td><td>0.24%</td></tr><tr><td>Logit proportion</td><td>-4.60%</td><td>-4.60%</td><td></td><td></td><td>-3.94%</td><td>-3.94%</td><td></td><td></td><td>0.73</td><td>0.73</td><td></td><td></td><td>-6.91%</td><td>-6.91%</td><td>-2.94%</td></tr><tr><td rowspan="3">Logit mean value</td><td>-4.36</td><td>-4.36</td><td></td><td></td><td>-3.83</td><td>-3.83</td><td></td><td></td><td>0.22</td><td>0.21</td><td></td><td></td><td>-6.53</td><td>-6.53</td><td>-2.60</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.14</td><td>0.14</td><td></td><td></td><td>0.11</td><td>0.11</td><td></td><td></td><td>0.03</td><td>0.03</td><td></td><td></td><td>0.21</td><td>0.11</td><td>-2.61</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.03</td><td>0.03</td><td></td><td></td><td>0.04</td><td>0.04</td><td></td><td></td><td>0.02</td><td>0.02</td><td></td><td></td><td>0.05</td><td>0.04</td><td>0.04</td></tr><tr><td rowspan="7">Horse</td><td>Number of labs</td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td></tr><tr><td>Number of labs after<br>outlier elimination</td><td>10</td><td>10</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td></tr><tr><td>Proportion (v/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td>1.31%</td><td>1.31%</td><td></td><td></td><td>1.89%</td><td>1.89%</td><td></td><td></td><td>60.3%</td><td>60.0%</td><td></td><td></td><td>0.47%</td><td>0.47%</td><td>30%</td></tr><tr><td>S<sub>R</sub></td><td>0.17%</td><td>0.17%</td><td></td><td></td><td>0.37%</td><td>0.37%</td><td></td><td></td><td>1.1%</td><td>1.1%</td><td></td><td></td><td>0.08%</td><td>0.08%</td><td>41.9%</td></tr><tr><td>S<sub>r</sub></td><td>0.05%</td><td>0.05%</td><td></td><td></td><td>0.14%</td><td>0.14%</td><td></td><td></td><td>0.3%</td><td>0.3%</td><td></td><td></td><td>0.04%</td><td>0.04%</td><td>1.9%</td></tr><tr><td>Logit proportion</td><td>-4.60%</td><td>-4.60%</td><td></td><td></td><td>-3.94</td><td>-3.94</td><td></td><td></td><td>0.67</td><td>0.67</td><td></td><td></td><td>-5.29</td><td>-5.29</td><td>0.7%</td></tr><tr><td rowspan="3">Logit mean value</td><td>-4.32</td><td>-4.32</td><td></td><td></td><td>-3.95</td><td>-3.95</td><td></td><td></td><td>0.42</td><td>0.40</td><td></td><td></td><td>-5.35</td><td>-5.36</td><td>-0.85%</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.13</td><td>0.13</td><td></td><td></td><td>0.20</td><td>0.20</td><td></td><td></td><td>0.05</td><td>0.05</td><td></td><td></td><td>0.18</td><td>0.17</td><td>-0.32%</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.04</td><td>0.04</td><td></td><td></td><td>0.08</td><td>0.08</td><td></td><td></td><td>0.01</td><td>0.01</td><td></td><td></td><td>0.08</td><td>0.08</td><td>-0.33%</td></tr><tr><td rowspan="7">Turkey</td><td>Number of labs</td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td></tr><tr><td>Number of labs after<br>outlier elimination</td><td>10</td><td>10</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td></tr><tr><td>Proportion (v/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td>1.12%</td><td>1.12%</td><td></td><td></td><td>0.42%</td><td>0.42%</td><td></td><td></td><td>1.86%</td><td>1.85%</td><td></td><td></td><td>54.0%</td><td>53.8%</td><td>4.03%</td></tr><tr><td>S<sub>R</sub></td><td>0.16%</td><td>0.16%</td><td></td><td></td><td>0.09%</td><td>0.09%</td><td></td><td></td><td>0.19%</td><td>0.19%</td><td></td><td></td><td>2.1%</td><td>2.0%</td><td>0.46%</td></tr><tr><td>S<sub>r</sub></td><td>0.05%</td><td>0.05%</td><td></td><td></td><td>0.04%</td><td>0.04%</td><td></td><td></td><td>0.07%</td><td>0.07%</td><td></td><td></td><td>0.9%</td><td>0.9%</td><td>0.14%</td></tr><tr><td>Logit proportion</td><td>-4.60</td><td>-4.60</td><td></td><td></td><td>-5.29</td><td>-5.29</td><td></td><td></td><td>-3.94</td><td>-3.94</td><td></td><td></td><td>0.73</td><td>0.73</td><td>-2.94%</td></tr><tr><td rowspan="3">Logit mean value</td><td>-4.48</td><td>-4.48</td><td></td><td></td><td>-5.47</td><td>-5.47</td><td></td><td></td><td>-3.97</td><td>-3.97</td><td></td><td></td><td>0.16</td><td>0.15</td><td>-3.17%</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.14</td><td>0.14</td><td></td><td></td><td>0.22</td><td>0.22</td><td></td><td></td><td>0.10</td><td>0.10</td><td></td><td></td><td>0.08</td><td>0.12</td><td>-3.17%</td></tr><tr><td>Logit s<sub>r</sub></td><td>0.05</td><td>0.05</td><td></td><td></td><td>0.10</td><td>0.10</td><td></td><td></td><td>0.04</td><td>0.04</td><td></td><td></td><td>0.04</td><td>0.04</td><td>0.04%</td></tr></tbody></table>
<table><caption>Table 4. Cont.</caption><thead><tr><th rowspan="3">Species</th><th colspan="12">Sample</th></tr><tr><th colspan="3">1</th><th colspan="3">2</th><th colspan="3">3</th><th colspan="3">4</th><th colspan="3">5</th><th colspan="3">6</th><th colspan="3">7</th></tr><tr><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th></tr></thead><tbody><tr><td rowspan="6">Beef</td><td>Number of labs</td><td>11</td><td></td><td>11</td><td>11</td><td></td><td>11</td><td></td><td>11</td><td>11</td><td>30%</td><td>33.02%</td><td>32.87%</td><td>2.79%</td><td>1.9%</td><td>0.91%</td><td>0.5%</td><td>48.7%</td><td>50%</td><td>48.7%</td></tr><tr><td>Number of labs after<br/>outlier elimination</td><td>10</td><td>1%</td><td>10</td><td>11</td><td>65.7%</td><td>66.7%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proportion (ν/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td></td><td>1.58%</td><td>1.58%</td><td>66.9%</td><td>2.8%</td><td>2.8%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>R</sub></td><td></td><td>0.13%</td><td>0.13%</td><td>2.8%</td><td>1.0%</td><td>1.0%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>r</sub></td><td></td><td>0.04%</td><td>0.04%</td><td>0.65%</td><td>0.65%</td><td>0.65%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Logit proportion</td><td>Logit mean value</td><td></td><td>-4.60</td><td>-4.60</td><td>0.70</td><td>0.69</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>-4.13</td><td>-4.13</td><td>0.13</td><td>0.13</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.08</td><td>0.08</td><td>0.04</td><td>0.04</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.02</td><td>0.02</td><td>0.04</td><td>0.04</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="6">Sheep</td><td>Number of labs</td><td>11</td><td>1%</td><td>11</td><td>11</td><td></td><td>1.9%</td><td>2.19%</td><td>0.14%</td><td>0.14%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of labs after<br/>outlier elimination</td><td>10</td><td>1%</td><td>10</td><td>11</td><td>11</td><td>0.5%</td><td>0.22%</td><td>0.01%</td><td>0.01%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proportion (ν/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td></td><td>1.50%</td><td>1.49%</td><td>2.21%</td><td>0.14%</td><td>0.14%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>R</sub></td><td></td><td>0.15%</td><td>0.14%</td><td>0.22%</td><td>0.13%</td><td>0.13%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>r</sub></td><td></td><td>0.03%</td><td>0.03%</td><td>0.14%</td><td>0.13%</td><td>0.13%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Logit proportion</td><td>Logit mean value</td><td></td><td>-4.60</td><td>-4.60</td><td>-3.94</td><td>-3.94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>-4.18</td><td>-4.19</td><td>-3.79</td><td>-3.80</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.10</td><td>0.10</td><td>0.10</td><td>0.10</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.02</td><td>0.02</td><td>0.06</td><td>0.06</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="6">Pig</td><td>Number of labs</td><td>11</td><td>94%</td><td>11</td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td>11</td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td>11</td></tr><tr><td>Number of labs after<br/>outlier elimination</td><td>10</td><td>10</td><td>10</td><td>11</td><td>11</td><td>30%</td><td>28.5%</td><td>2.2%</td><td>2.2%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proportion (ν/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td></td><td>91.77%</td><td>91.74%</td><td>28.5%</td><td>0.12%</td><td>0.12%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>R</sub></td><td></td><td>0.83%</td><td>0.83%</td><td>2.2%</td><td>0.7%</td><td>0.7%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>S<sub>r</sub></td><td></td><td>0.16%</td><td>0.16%</td><td>0.7%</td><td>0.7%</td><td>0.7%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Logit proportion</td><td>Logit mean value</td><td></td><td>2.75</td><td>2.75</td><td>-0.85</td><td>-0.85</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>2.41</td><td>2.41</td><td>-0.92</td><td>-0.92</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.11</td><td>0.11</td><td>0.11</td><td>0.11</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Logit s<sub>r</sub></td><td></td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 4. Cont.</caption><thead><tr><th rowspan="3">Species</th><th rowspan="3">Parameter <sup>1</sup></th><th colspan="12">Sample</th></tr><tr><th colspan="2">1</th><th colspan="2">2</th><th colspan="2">3</th><th colspan="2">4</th><th colspan="2">5</th><th colspan="2">6</th><th colspan="2">7</th></tr><tr><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th><th>AGES</th><th>NCBI</th></tr></thead><tbody><tr><td rowspan="7">Goat</td><td>Number of labs</td><td>11</td><td>11</td><td></td><td></td><td>11</td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td></td></tr><tr><td>Number of labs after<br>outlier elimination</td><td>10</td><td>10</td><td></td><td></td><td>11</td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td>11</td><td></td><td></td></tr><tr><td>Proportion (v/m)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean value</td><td>1.44%</td><td>1.45%</td><td></td><td></td><td>35.4%</td><td>35.6%</td><td>2.00%</td><td>2.01%</td><td>0.09%</td><td>0.1%</td><td>0.09%</td><td>0.09%</td><td></td></tr><tr><td>S<sub>r</sub></td><td>0.14%</td><td>0.14%</td><td></td><td></td><td>1.0%</td><td>1.0%</td><td>022%</td><td>0.23%</td><td>0.02%</td><td>0.02%</td><td>0.02%</td><td>0.02%</td><td></td></tr><tr><td>Logit proportion</td><td>0.05%</td><td>0.05%</td><td></td><td></td><td>0.3%</td><td>0.3%</td><td>0.10%</td><td>0.10%</td><td>0.02%</td><td>0.02%</td><td>0.02%</td><td>0.02%</td><td></td></tr><tr><td>Logit mean value</td><td>-4.60</td><td>-4.60</td><td></td><td></td><td>0.85</td><td>-0.85</td><td>-3.94</td><td>-3.94</td><td>-6.91</td><td>-6.91</td><td>-6.91</td><td>-6.99</td><td></td></tr><tr><td rowspan="3">Logit s<sub>r</sub></td><td>-4.23</td><td>-4.22</td><td></td><td></td><td>-0.60</td><td>-0.59</td><td>-3.89</td><td>-3.89</td><td>-7.05</td><td>-7.05</td><td>-6.99</td><td>-6.99</td><td></td></tr><tr><td>0.10</td><td>0.10</td><td></td><td></td><td>0.04</td><td>0.05</td><td>0.11</td><td>0.11</td><td>0.19</td><td>0.19</td><td>0.19</td><td>0.19</td><td></td></tr><tr><td>0.04</td><td>0.04</td><td></td><td></td><td>0.01</td><td>0.01</td><td>0.05</td><td>0.05</td><td>0.19</td><td>0.19</td><td>0.17</td><td></td><td></td></tr></tbody></table>

1 s<sub>r</sub>: reproducibility standard deviation; s<sub>r</sub>: repeatability standard deviation; v: volume; m: mass.
As expected, the reproducibility standard deviation and repeatability standard deviation were found to be higher for lower proportions of animal species than for proportions of about 50% (logit = 0), independent of the database selected for alignment. It was found that pig tends to have higher standard deviations in reproducibility, but not in repeatability, compared to other animal species (Figure 2A). A tendency towards higher reproducibility standard deviations was also observed in case beef was the predominant animal species in the sample. This also held true for the repeatability standard deviation, although to a lower extent.

The bias between the proportion of the animal species added to the sample and the overall mean determined in the ring trial was in the range from -0.6 to 0.6 logits, with just two exceptions. Neither the animal species nor the proportion of the animal species was found to have a systematic effect on the bias. The animal species and the proportion of the animal species did not have a systematic impact on the reproducibility standard deviation either.

Thus, the standard deviation induced by the bias ("bias standard deviation"), absolute reproducibility standard deviation, and absolute repeatability standard deviation could be modeled across animal species and samples, for both the AGES and NCBI databases. The modeled variance function was similar for both databases. The lowest bias standard deviation, reproducibility standard deviation, and repeatability standard deviation were found for a proportion of 50% (logit = 0). The closer the proportion to 0% or 100%, the higher the standard deviations. Table 5 summarizes the modeled and re-transformed standard deviations, which were found to be independent of the database used for alignment.

<table><caption>**Table 5.** Bias standard deviation, reproducibility standard deviation, and repeatability standard deviation (absolute, i.e., retransformed to proportions of animal species) depending on the proportion of animal species.</caption><thead><tr><th rowspan="2">Standard Deviation</th><th colspan="2">Proportion of Animal Species</th></tr><tr><th>5%/95%</th><th>50%</th></tr></thead><tbody><tr><td>Absolute bias standard deviation</td><td>1.8%</td><td>7.2%</td></tr><tr><td>Absolute reproducibility standard deviation</td><td>0.5%</td><td>1.8%</td></tr><tr><td>Absolute repeatability standard deviation</td><td>0.2%</td><td>0.6%</td></tr></tbody></table>

### 3.1.7. Variability across Animal Species and Measuring Uncertainty

To allow predictions for further analyses, the measuring uncertainty was evaluated. Since the database (AGES or NCBI) was not found to have an impact on bias standard deviation, reproducibility standard deviation, or repeatability standard deviation, the measuring uncertainty was only evaluated for the AGES database, representative for both databases.

Based on the reproducibility standard deviation, a prediction profile was established in terms of the 95% confidence interval of the results of all laboratories across all animal species. In addition, the 95% confidence interval was established by considering both the reproducibility standard deviation and the bias standard deviation.

The upper part of Figure 3 shows the 95% confidence interval of the (outlier-cleaned) results depending on the respective overall mean value of the proportion of an animal species (without considering the bias). The left side of the figure shows the entire range, and the right side an enlarged view of proportions from 0 to 10%. The figure indicates that for a "true proportion" (assuming that the overall mean across laboratories applying Illumina platforms reflects the "true proportion") of, e.g., 5%, the 95% confidence interval is 4.1-6.2%. In total, 4.7% of the individual values are outside the 95% confidence interval.
<figure><img src="image_60.png" /><figcaption>**Figure 3.** 95% confidence interval for the mean (top) and added (bottom) proportion of animal species based on a single measurement, independent of the animal species.</figcaption></figure>

The 95% confidence interval of the (outlier-cleaned) results of all laboratories, depending on the respective proportion added (by considering the bias) is shown in the lower part of Figure 3. The left and right sides show the entire range and an enlarged view of proportions from 0 to 10%, respectively. For example, for an added proportion of 5%, the 95% confidence interval is 2.5–9.9%. In total, 3.7% of the individual values are outside the 95% confidence interval.

From the prediction profiles, a measurement uncertainty profile was established, indicating how far the proportion determined may deviate from the “true” proportion of the animal species. Figure 4 shows the 95% measurement uncertainty intervals depending on the proportion of the animal species determined, on the left side without consideration of the bias (assuming that the “true” proportion equals the overall mean across laboratories applying Illumina platforms), on the right side under consideration of the bias (assuming that the “true” proportion equals the proportion of animal species added to the sample).
<figure><img src="image_61.png" /><figcaption>**Figure 4.** 95% measuring uncertainty interval for the proportion of the animal species determined, based on a single measurement, independent of the animal species.</figcaption></figure>

For a 50% proportion of the animal species, the “true value” can be 3.6% (percentage points) lower or higher, if the bias is not taken into account, or even 15.2%, if the bias is taken into consideration.

### 3.1.8. z Scores

z scores were calculated for interlaboratory evaluation across samples and animal species (Figure 5). z scores measure standardized deviations of laboratory mean values from the overall mean value. An absolute z score > 2 hints at a statistically significant deviation of the respective laboratory.

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /><figcaption>**Figure 5.** z scores for determination of proportions of animal species. (A) AGES database, (B) NCBI database. Absolute z scores < 2 are shown in blue, absolute z scores > 2 in red.</figcaption></figure>
LASSO regression was applied to check whether absolute z scores depended on the instrument (MiSeq, iSeq 100), NGS experience of the respective laboratory, and/or activation of the function "adapter trimming" before starting the sequencing run. Analyses were performed excluding data previously identified as outliers. NGS experience of the laboratory was found to significantly affect the z score. For laboratories more experienced in NGS (laboratories 01, 04, 08, 15, 20), lower absolute z scores were determined compared to those with lower NGS experience. By contrast, neither the instrument (MiSeq, iSeq 100) nor activation of the function "adapter trimming" before starting the sequencing run was found to significantly affect the z score.

## 3.2. Qualitative Evaluation of Ring Trial Data

### 3.2.1. False Positive Rate

Next, it was investigated whether animal species were identified that had not been added to the samples, and whether the false positive rate depended on the database selected for alignment. A sample was classified as positive if for at least one animal species that had not been added, a proportion above a defined threshold was obtained. The threshold was set to 0.1%, 0.5%, 1.0%, or 2.0% proportion of the animal species.

Table 6 indicates that alignment against the AGES database (Table 6A) resulted in less false positive reads compared to alignment against the NCBI database (Table 6B).

<table><caption>**Table 6.** False positive reads obtained for samples 01–07. (A): AGES database, (B): NCBI database. Laboratories 01–06, 08, 14, 15: MiSeq; laboratories 07, 13, 20: iSeq 100.</caption><thead><tr><th rowspan="3">Sample</th><th rowspan="3">Subsample</th><th colspan="10">(A)</th></tr><tr><th colspan="10">Laboratory</th></tr><tr><th>01</th><th>02</th><th>03</th><th>04</th><th>06</th><th>07</th><th>08</th><th>13</th><th>14</th><th>15</th><th>20</th></tr></thead><tbody><tr><td rowspan="2">1</td><td>A</td><td>6</td><td>2</td><td>1</td><td>-</td><td>2</td><td>-</td><td>2</td><td>-</td><td>2</td><td>2</td><td>-</td></tr><tr><td>B</td><td>2</td><td>5</td><td>-</td><td>3</td><td>3</td><td>-</td><td>-</td><td>1</td><td>4</td><td>-</td><td>-</td></tr><tr><td rowspan="2">2</td><td>A</td><td>99</td><td>41</td><td>21</td><td>95</td><td>78</td><td>6</td><td>76</td><td>41</td><td>44</td><td>87</td><td>5</td></tr><tr><td>B</td><td>43</td><td>89</td><td>31</td><td>121</td><td>73</td><td>8</td><td>74</td><td>43</td><td>34</td><td>121</td><td>-</td></tr><tr><td rowspan="2">3</td><td>A</td><td>49</td><td>325</td><td>14</td><td>1234</td><td>177</td><td>91</td><td>47</td><td>359</td><td>91</td><td>88</td><td>64</td></tr><tr><td>B</td><td>51</td><td>369</td><td>40</td><td>1356</td><td>196</td><td>85</td><td>44</td><td>355</td><td>79</td><td>52</td><td>35</td></tr><tr><td rowspan="2">4</td><td>A</td><td>55</td><td>5</td><td>12</td><td>38</td><td>106</td><td>69</td><td>57</td><td>3</td><td>90</td><td>57</td><td>10</td></tr><tr><td>B</td><td>38</td><td>34</td><td>20</td><td>64</td><td>117</td><td>37</td><td>80</td><td>10</td><td>100</td><td>67</td><td>10</td></tr><tr><td rowspan="2">5</td><td>A</td><td>6</td><td>51</td><td>2</td><td>286</td><td>173</td><td>121</td><td>56</td><td>124</td><td>271</td><td>79</td><td>65</td></tr><tr><td>B</td><td>9</td><td>57</td><td>3</td><td>286</td><td>212</td><td>62</td><td>66</td><td>137</td><td>396</td><td>39</td><td>32</td></tr><tr><td rowspan="2">6</td><td>A</td><td>8</td><td>37</td><td>2</td><td>111</td><td>274</td><td>80</td><td>110</td><td>33</td><td>161</td><td>41</td><td>64</td></tr><tr><td>B</td><td>5</td><td>50</td><td>7</td><td>102</td><td>314</td><td>46</td><td>106</td><td>50</td><td>169</td><td>71</td><td>27</td></tr><tr><td rowspan="2">7</td><td>A</td><td>45</td><td>188</td><td>42</td><td>608</td><td>99</td><td>62</td><td>69</td><td>250</td><td>61</td><td>62</td><td>39</td></tr><tr><td>B</td><td>80</td><td>717</td><td>42</td><td>596</td><td>140</td><td>83</td><td>86</td><td>294</td><td>49</td><td>28</td><td>25</td></tr></tbody></table>

<table><thead><tr><th rowspan="3">Sample</th><th rowspan="3">Subsample</th><th colspan="10">(B)</th></tr><tr><th colspan="10">Laboratory</th></tr><tr><th>01</th><th>02</th><th>03</th><th>04</th><th>06</th><th>07</th><th>08</th><th>13</th><th>14</th><th>15</th><th>20</th></tr></thead><tbody><tr><td rowspan="2">1</td><td>A</td><td>104</td><td>75</td><td>122</td><td>49</td><td>72</td><td>40</td><td>65</td><td>20</td><td>48</td><td>72</td><td>19</td></tr><tr><td>B</td><td>120</td><td>91</td><td>164</td><td>59</td><td>81</td><td>24</td><td>58</td><td>35</td><td>29</td><td>54</td><td>25</td></tr><tr><td rowspan="2">2</td><td>A</td><td>630</td><td>715</td><td>612</td><td>743</td><td>804</td><td>242</td><td>891</td><td>465</td><td>526</td><td>637</td><td>388</td></tr><tr><td>B</td><td>489</td><td>783</td><td>1047</td><td>753</td><td>898</td><td>388</td><td>752</td><td>540</td><td>432</td><td>682</td><td>345</td></tr><tr><td rowspan="2">3</td><td>A</td><td>387</td><td>766</td><td>456</td><td>1639</td><td>497</td><td>394</td><td>209</td><td>583</td><td>355</td><td>297</td><td>242</td></tr><tr><td>B</td><td>241</td><td>798</td><td>633</td><td>1792</td><td>606</td><td>383</td><td>229</td><td>621</td><td>365</td><td>177</td><td>127</td></tr><tr><td rowspan="2">4</td><td>A</td><td>2133</td><td>400</td><td>2738</td><td>1482</td><td>1908</td><td>1095</td><td>2214</td><td>670</td><td>1164</td><td>2428</td><td>585</td></tr><tr><td>B</td><td>1584</td><td>2043</td><td>2896</td><td>1717</td><td>1941</td><td>1477</td><td>2182</td><td>837</td><td>1390</td><td>2585</td><td>668</td></tr></tbody></table>
<table><caption>Table 6. *Cont.*</caption><thead><tr><th colspan="10">(B)</th></tr><tr><th rowspan="2">Sample</th><th rowspan="2">Subsample</th><th colspan="9">Laboratory</th></tr><tr><th>01</th><th>02</th><th>03</th><th>04</th><th>06</th><th>07</th><th>08</th><th>13</th><th>14</th><th>15</th><th>20</th></tr></thead><tbody><tr><td rowspan="2">5</td><td>A</td><td>987</td><td>846</td><td>1963</td><td>1127</td><td>1259</td><td>1177</td><td>988</td><td>531</td><td>863</td><td>924</td><td>487</td></tr><tr><td>B</td><td>1711</td><td>1179</td><td>2261</td><td>1063</td><td>1368</td><td>468</td><td>1202</td><td>628</td><td>1165</td><td>547</td><td>297</td></tr><tr><td rowspan="2">6</td><td>A</td><td>501</td><td>590</td><td>2354</td><td>634</td><td>796</td><td>584</td><td>556</td><td>437</td><td>525</td><td>371</td><td>261</td></tr><tr><td>B</td><td>627</td><td>580</td><td>2619</td><td>584</td><td>853</td><td>394</td><td>518</td><td>440</td><td>612</td><td>543</td><td>200</td></tr><tr><td rowspan="2">7</td><td>A</td><td>250</td><td>452</td><td>496</td><td>788</td><td>322</td><td>210</td><td>426</td><td>459</td><td>244</td><td>229</td><td>175</td></tr><tr><td>B</td><td>260</td><td>950</td><td>422</td><td>819</td><td>627</td><td>366</td><td>388</td><td>519</td><td>245</td><td>192</td><td>132</td></tr></tbody></table>

Alignment against the AGES database only yielded false positive samples at threshold values of 0.05% or 0.1% (Figure 6).

<figure><img src="image_64.png" /><figcaption>Figure 6. False positive rates by sample, depending on the defined threshold for the AGES (**left**) and the NCBI (**right**) database.</figcaption></figure>

Higher false positive rates for the NCBI database were inevitably caused by the higher number of entries in the NCBI database compared to the AGES database. Most species resulting in false positive reads when using the NCBI database were not contained in the AGES database and thus could not be identified with the latter.

From the ring trial data it can be concluded that by using an appropriate customized database and by setting the threshold to 0.5%, false positive rates < 1% will be obtained.

### 3.2.2. False Negative Rate

Next, the false negative rate was evaluated at threshold values of 0.05%, 0.1%, 0.5%, and 1% for both the AGES and the NCBI databases (Table 7).
<table><caption>Table 7. Proportion of results below a thresholds of 0.05%, 0.1%, 0.5%, and 1%.</caption><thead><tr><th rowspan="3">Sample</th><th rowspan="3">Species</th><th colspan="8">Proportion of Results below a Threshold of</th></tr><tr><th colspan="2">Spiking Level (%)</th><th colspan="2">AGES Database</th><th colspan="2">1%</th><th colspan="2">0.5%</th><th colspan="2">NCBI Database</th></tr><tr><th>0.05%</th><th>0.1%</th><th>0.5%</th><th>1%</th><th>0.05%</th><th>0.1%</th><th>0.5%</th><th>1%</th></tr></thead><tbody><tr><td rowspan="6">1</td><td>Pig</td><td>94</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Chicken</td><td>1</td><td>-</td><td>-</td><td>-</td><td>1/22 (5%)</td><td>-</td><td>-</td><td>-</td><td>1/22 (5%)</td></tr><tr><td>Horse</td><td>1</td><td>-</td><td>-</td><td>-</td><td>1/22 (5%)</td><td>-</td><td>-</td><td>-</td><td>1/22 (5%)</td></tr><tr><td>Turkey</td><td>1</td><td>-</td><td>-</td><td>-</td><td>5/22 (23%)</td><td>-</td><td>-</td><td>-</td><td>6/22 (27%)</td></tr><tr><td>Beef</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Sheep</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="6">2</td><td>Goat</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Beef</td><td>65.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Pig</td><td>30.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Horse</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Sheep</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Turkey</td><td>0.5</td><td>-</td><td>-</td><td>17/22 (77%)</td><td>22/22 (100%)</td><td>-</td><td>-</td><td>17/22 (77%)</td><td>22/22 (100%)</td></tr><tr><td rowspan="4">3</td><td>Horse</td><td>66.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Goat</td><td>30.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Chicken</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Turkey</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="6">4</td><td>Sheep</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Beef</td><td>0.1</td><td>14/22 (64%)</td><td>20/22 (91%)</td><td>13/22 (59%)</td><td>22/22 (100%)</td><td>14/22 (64%)</td><td>20/22 (91%)</td><td>14/22 (64%)</td><td>22/22 (100%)</td></tr><tr><td>Goat</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Horse</td><td>0.1</td><td>-</td><td>-</td><td>22/22 (100%)</td><td>22/22 (100%)</td><td>-</td><td>-</td><td>22/22 (100%)</td><td>22/22 (100%)</td></tr><tr><td>Pig</td><td>0.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="4">5</td><td>Chicken</td><td>67.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Sheep</td><td>30.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Beef</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Pig</td><td>0.5</td><td>-</td><td>-</td><td>4/22 (18%)</td><td>22/22 (100%)</td><td>-</td><td>-</td><td>4/22 (18%)</td><td>22/22 (100%)</td></tr><tr><td rowspan="2">6</td><td>Goat</td><td>0.1</td><td>-</td><td>-</td><td>15/22 (68%)</td><td>22/22 (100%)</td><td>-</td><td>-</td><td>12/22 (55%)</td><td>22/22 (100%)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 7. Cont.</caption><thead><tr><th rowspan="3">Sample</th><th rowspan="3">Species</th><th rowspan="3">Spiking Level (%)</th><th colspan="6">Proportion of Results below a Threshold of</th></tr><tr><th colspan="2">AGES Database</th><th rowspan="2">1%</th><th rowspan="2">0.05%</th><th rowspan="2">0.1%</th><th rowspan="2">NCBI Database</th></tr><tr><th>0.05%</th><th>0.1%</th></tr></thead><tbody><tr><td rowspan="5">6</td><td>Turkey</td><td>67.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Horse</td><td>30.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Pig</td><td>1.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Beef</td><td>0.5</td><td>-</td><td>-</td><td>19/22 (86%)</td><td>-</td><td>-</td><td>-</td><td>19/22 (86%)</td></tr><tr><td>Chicken</td><td>0.1</td><td>-</td><td>-</td><td>22/22 (100%)</td><td>22/22 (100%)</td><td>-</td><td>22/22 (100%)</td><td>22/22 (100%)</td></tr><tr><td rowspan="4">7</td><td>Beef</td><td>50.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Pig</td><td>40.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Chicken</td><td>5.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Turkey</td><td>5.0</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>