Droplet digital polymerase chain reaction (ddPCR) assays integrated with an internal control for quantification of bovine, porcine, chicken and turkey species in food and feed
Droplet digital polymerase chain reaction (ddPCR) assays integrated with an internal control for quantification of bovine, porcine, chicken and turkey species in food and feed
Hanan R. Shehata 0 1 2
Jiping Li 0 2
Shu Chen 0 2
Helen Redda 0 2 3
Shumei Cheng 0 2 3
Nicole Tabujara 0 2
Honghong Li 0 2
Keith Warriner 0 2 3
Robert Hanner 0 1 2
0 Current address: College of Food Science and Technology, Agriculture University of Hebei , Baoding, Hebei , P. R. China
1 Department of Integrative Biology, University of Guelph , Guelph, Ontario , Canada , 2 Biodiversity Institute of Ontario, University of Guelph , Guelph, Ontario , Canada , 3 Microbiology Department, Mansoura University, Mansoura, Egypt, 4 Laboratory Services Division, University of Guelph , Guelph, Ontario , Canada
2 Editor: Diego Breviario, Istituto di Biologia e Biotecnologia Agraria Consiglio Nazionale delle Ricerche , ITALY
3 Department of Food Science, University of Guelph , Guelph, Ontario , Canada
Food adulteration and feed contamination are significant issues in the food/feed industry, especially for meat products. Reliable techniques are needed to monitor these issues. Droplet Digital PCR (ddPCR) assays were developed and evaluated for detection and quantification of bovine, porcine, chicken and turkey DNA in food and feed samples. The ddPCR methods were designed based on mitochondrial DNA sequences and integrated with an artificial recombinant plasmid DNA to control variabilities in PCR procedures. The specificity of the ddPCR assays was confirmed by testing both target species and additional 18 nontarget species. Linear regression established a detection range between 79 and 33200 copies of the target molecule from 0.26 to 176 pg of fresh animal tissue DNA with a coefficient of determination (R2) of 0.997±0.999. The quantification ranges of the methods for testing fortified heat-processed food and feed samples were 0.05±3.0% (wt/wt) for the bovine and turkey targets, and 0.01±1.0% (wt/wt) for pork and chicken targets. Our methods demonstrated acceptable repeatability and reproducibility for the analytical process for food and feed samples. Internal validation of the PCR process was monitored using a control chart for 74 consecutive ddPCR runs for quantifying bovine DNA. A matrix effect was observed while establishing calibration curves with the matrix type under testing, and the inclusion of an internal control in DNA extraction provides a useful means to overcome this effect. DNA degradation caused by heating, sonication or Taq I restriction enzyme digestion was found to reduce ddPCR readings by as much as 4.5 fold. The results illustrated the applicability of the methods to quantify meat species in food and feed samples without the need for a standard curve, and to potentially support enforcement activities for food authentication and feed control. Standard reference materials matching typical manufacturing processes are needed for future validation of ddPCR assays for absolute quantification of meat species.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors gratefully acknowledge the
financial support (for preparation of the
manuscript) provided by the Canadian Food
Inspection Agency (CFIA) (via the Federal
Assistance Program) to RHH for HS, and in-kind
contributions from Laboratory Services Division,
University of Guelph, for the development and
validation of the methods.
Food adulteration or mislabeling remains a key challenge to the food industry [
majority of food adulteration is economically motivated whereby low cost ingredients are substituted
for high value products [
]. Beyond the economic impact of food fraud, issues of food safety,
food contamination and religious restrictions are also relevant [1±3]. Amongst the commodity
groups, meat represents one of the most common foods implicated in food fraud [
example, 2013 witnessed the largest single case of food fraud when horse meat was substituted
for beef in meat products distributed in the United Kingdom and Ireland [
]. Other studies
reported a high incidence of meat substitution, including a 57% substitution rate in processed
meat products, a 54% substitution rate in chicken sausages from Italian markets [
], and a
78% substitution rate in beef and poultry products in Malaysia . Examples of substitution
included beef and pork in chicken products, and pork in beef products [
]. With respect to
animal feed, the inclusion of prohibited species, such as bovine tissues, represents a feed safety
issue due to the potential transfer of prions. Feed control regulations have been enforced in
many countries since the ruminant feed ban in 1997 in order to minimize the risks of
spreading of bovine spongiform encephalopathy (BSE) [
]. The feed industry produces millions
of tons of feed, as well as similar amounts of ruminant animal protein-based materials, for
non-ruminant feed or other uses each year. Risks of cross-contamination exist on farms and
throughout the various stages of production and shipment.
In order to protect the food and feed industry and consumers, regulatory bodies need to
monitor food authenticity and feed contamination by ruminant materials. Reliable, rapid and
accurate methods are needed for use in food authentication and in feed control [
techniques have been used for meat species identification, including chromatographic,
immunological and electrophoretic methods, but these methods are limited due to their low
accuracy, low sensitivity and time consuming processes [9±12]. Furthermore, some of these
techniques are unreliable for use with processed or cooked meat [
]. Conventional PCR
has been widely used as a qualitative measure of a target DNA whereas quantitative real-time
PCR (qPCR) can also quantify copies of the target sequence [2, 8, 9, 12±22]. A recently
developed PCR technology is digital PCR (dPCR) which provides more accurate quantification of
target DNA . In dPCR, the PCR reaction mixture is partitioned into tens of thousands of
droplets with each droplet harboring an independent PCR reaction. The target DNA copy
number is determined based on the number of droplets positive for amplification of the target
4, 24, 25
]. The dPCR approach provides absolute DNA quantification, eliminates the
need for a standard curve as in qPCR, and improves accuracy for quantifying target DNA
especially at low concentrations or in a high background of foreign DNA [
]. Many dPCR
methods have been developed for quantifying clinical diagnostic targets [
] and for viral,
bacterial and parasitic pathogens [29±32]. Recently, dPCR methods have also been reported
for quantification of genetically modified organisms (GMO) in food and feed samples [
Additionally some ddPCR assays were developed for pork and chicken in meat products using
non- mitochondrial genes [
The objective of this study is to develop, optimize and validate common droplet digital PCR
(ddPCR) assays integrated with an internal control for detection and quantification of bovine,
porcine, chicken and turkey species in meat products and animal feed samples. Mitochondrial
DNA was selected as the PCR target due to its high copy numbers present in cells, facilitating
detection of trace quantities in food and feed matrices, especially when these commodities
may be subject to extensive processing. To our knowledge, this is the first comprehensive
report to describe ddPCR assays integrated with an internal control to quantify bovine,
chicken, porcine and turkey DNA in food and feed samples.
2 / 17
Materials and methods
All fresh raw animal tissue samples, raw milk, blood and plant samples were obtained either
from a local grocery store or from Animal Health Laboratory Services, University of Guelph,
Ontario. Dry feed samples were obtained from local feed processing plants in Ontario. Pure
fresh beef, pork, chicken and turkey muscle tissues and poultry and pork meal feed samples
were used as reference materials. The species identities of all materials were confirmed by
DNA barcoding based on the standard CO1 gene [
]. The feed samples were confirmed
negative for the detection target by PCR for use as matrices for spiking experiments. Fortified
samples were prepared from single species materials at known concentrations of the targets
and were used as positive controls. A rabbit or fish muscle tissue sample, an unfortified feed
sample matrix, reagent blank and PCR water were used as negative controls. An artificial
DNA fragment (134 bp) was cloned into a plasmid using pcr4 TOPO cloning kit (Applied
Biosystems, Foster City, CA) and used as an internal control (Table 1) [
]. The controls
were included in each ddPCR run. Additional non-target species samples used for specificity
testing included common birds, mammals, fish and plant species (duck, sheep, goat, dog,
horse, mouse, rat, rabbit, Pollock fish, salmon, sole fish, rice, corn, wheat and soybean,
3 / 17
Fresh meat samples were first trimmed of skin and excess fat, and deboned (if applicable), and
then cut into ~1.0±1.5 cm3 pieces. The meat pieces were placed in a Cuisinart grinder and
homogenized for 3±5 minutes. The homogenized samples were then used for DNA extraction.
Cooked meat was prepared by autoclaving the meat pieces for 15 minutes at 121ÊC, and 17.5
psig pressure. The meat samples were allowed to cool, homogenized using a Cuisinart grinder,
and then air dried for 72 hours at room temperature (approximately 22ÊC) or dried in an oven
at 70ÊC for 24 hours to a moisture level of 4±5%. After drying, the samples were ground using
a mortar and pestle and passed through a sieve (mesh no. 100) to obtain fine powder. The
moisture content was measured using an air-oven method following AOAC 983.18 [
sample preparation and AOAC 950.46 Part B , air drying, section (a) for sample testing.
Fortified food or feed samples were prepared in a mortar by mixing/homogenizing the
appropriate mass of the heat-processed and dried pure meat species powder (500 mg dry wt)
with a pure meat, food or feed sample of a different species (4500 mg or 9500 mg dry wt) to
obtain 10% and 5% of target species samples initially. Fortified samples at lower concentrations
were prepared by mixing 1000±2000 mg (dry wt) of a homogenate at a higher concentration
with 1000±4000 mg (dry wt) pure meat, food or feed powder samples accordingly. The samples
were portioned into appropriate numbers of 100 mg sub-samples in 1.5 mL micro-centrifuge
tubes for testing, or frozen in a -80ÊC freezer for testing at a later date.
Total genomic DNA was extracted from a sub-sample (100 mg) of a homogenized
representative food or feed specimen using DNeasy Blood and Tissue1 Kit (Qiagen, Mississauga, ON,
Canada) following the manufacturer's protocol. DNA concentrations and quality, including
A260 and A280, were measured using a NanoDrop ND-2000 UV Vis ND-2000
Spectrophotometer (Thermo Fisher Scientific, Ottawa, ON, Canada) and a Qubit Fluorometer and Qubit
dsDNA BR Assay Kit (Thermo Fisher Scientific). Extracted DNA samples were diluted to a
concentration of 10 ng/μL prior to use, or frozen in a -20ÊC freezer for use at a later date.
Primers and probes
Primers and probes (Table 1) were selected or designed based on the mitochondrial DNA
sequences of the bovine, porcine, chicken and turkey genomes, and 5'-nuclease assay
chemistry [36±38]. The primers and probes were designed using the Primer Express Software v3.0
(Applied Biosystems) and synthesized using an ABI 3900 HT synthesizer (Applied Biosystems)
at the Laboratory Services, University of Guelph, Guelph, ON. Probes were 5'-labeled with
6-carboxyfluorescein (6-FAM) or Cal Fluor Orange (for the internal control) as the reporter
and BHQ-1 as the 3'-labelled quencher. Target genes, primer names, primer sequences,
positions on reference sequences, accession numbers and amplicon length are provided in Table 1.
Droplet digital polymerase chain reaction (ddPCR)
To optimize internal control use in ddPCR assays, 10-fold serial dilutions of the internal
control plasmid DNA, corresponding to 0.06±60,000 fg/μL, were prepared, mixed with Bovine
DNA (2.0 ng/μL) in a 1:4 ratio, and tested using the ddPCR in duplicates. A higher level of the
IC (>4500 copies/PCR) caused competitive amplification with the target DNA (S1 Table),
while a low concentration IC was found not stable upon repeated freeze/thaw cycles. The
optimal level of the IC was determined to be approximately 1700 ±20% copies per PCR reaction,
which provided reliable ddPCR outputs for use as a quantitative measure for each of the
4 / 17
ddPCR reactions. The repeatability of the IC ddPCR was evaluated in 16 replicates (S1 Fig).
Furthermore, the specificity of the internal control primers and probes was confirmed as
described in the ddPCR assay evaluation section below.
The ddPCR reaction conditions were optimized using varied amounts of target DNA in the
presence of other non-target species. Optimization experiments included optimizing
annealing temperatures (53±63ÊC) using a Gradient T100 Thermal Cycler (Bio-Rad, Mississauga,
ON, Canada), and varied concentrations of primers (400±1000 nM), and probes (200 nMÐ
500 nM). The optimized PCR reaction mixture (25 μL/reaction) contained 1x ddPCR
Supermix for Probe (Bio-Rad), 96 nM each of the primers and 64 nM probe for the animal target, 40
nM each of the primers and 32 nM probe for the internal control, 1700 copies of internal
control plasmid DNA and 40±50 ng of template DNA. From each PCR reaction mixture, 20 μL
were mixed with 70 μL of Droplet Generation oil for Probes (Bio-Rad) in a DG8 Cartridge
(Bio-Rad). The cartridge was covered with a DG8 gasket for ddPCR and loaded into the
QX200 Droplet Generator (Bio-Rad) to generate PCR droplets. From each droplet mix, 20 μL
were then transferred to a 96-well PCR plate (Bio-Rad). The plate was sealed with a foil heat
seal using PX1™ PCR plate Sealer (Bio-Rad). PCR thermal cycling was conducted using a
GeneAmp™ PCR System 9700 (Applied Biosystems), following optimized cycling conditions: an
initial incubation at 95ÊC for 10 min, 48 cycles of 20 s at 95ÊC and 40 s at 59±60ÊC, followed by
a final incubation at 98ÊC for 10 min and holding at 10ÊC until reading time. The amplification
signals were read using the QX200™ Droplet Reader and analyzed using its associated
QuantaSoft software (Bio-Rad) and recorded as copies/μL with confidence intervals of 95%. The
results from 13,000 or more droplets were accepted and converted into % by weight or by
DNA mass for reporting. A ddPCR result was considered acceptable only if the IC gave the
expected output with a 20% variation.
ddPCR assay evaluation
To evaluate the ddPCR assays for testing food and feed samples, the assays were tested for
their specificity, quantification range, repeatability, reproducibility, matrix effect, robustness
and effect of DNA degradation. All experiments were conducted in duplicate unless otherwise
indicated. The specificity of the ddPCR assays, including the IC ddPCR, was confirmed in silico
and in vitro by testing both target and non-target species samples (Table 2). The target species
samples included raw and processed products from bovine, porcine, chicken and turkey
origins. The non-target samples included common animal, fish and plant species (duck, sheep,
goat, dog, horse, mouse, rat, rabbit, Pollock fish, salmon, sole fish, rice, corn, wheat and
soybean). To evaluate the linearity of the ddPCR assays, different concentrations of DNA were
used in multiple PCR reactions. The amounts of DNA used per reaction were three fold
dilutions between 1.3 and 320.0 pg for beef, 1.3 and106.8 pg for chicken, 2.2 and 532.0 pg for pork,
and, 0.26 and 64 pg for turkey. To test for the effect of tissue type on ddPCR, fresh bovine and
porcine skeletal muscle, liver, heart or kidney, were tested in parallel. The quantification range
and limit were determined using a series of bovine, porcine, chicken or turkey-fortified food
and feed samples with concentrations of 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 3%, 5% and
10.00% (wt/wt) and tested in 4 replicates. The repeatability of ddPCR (consistency of results
between DNA extraction and between PCR reactions) was determined using the fortified food
and feed samples at 5 different target concentrations within the quantification range in 4
replicates. Similarly, intra-lab validation was conducted using three independent trials performed
on different days by different operators with a total of 12 data points per concentration per
fortified sample for 5 different target concentrations per sample type. The matrix effect was
evaluated using cooked animal tissue spiked into different types of food and feed within their
5 / 17
aThe number (n) after an animal or food sample indicates that multiple (n) samples from different individual animals were tested.
bSpecies ID was confirmed for representative target species and all non-target animal and fish species by DNA barcoding.
respective quantification ranges. For example, chicken meat was spiked into pork summer
sausage, beef hot dog or beef and pork salami.
To test the robustness of the assays, the methods were evaluated under different conditions
that can be variable under laboratory practices, including DNA storage (fresh versus frozen for
3 weeks), prolonged PCR reagent shelf life (fresh versus old PCR master mix with a few days of
shelf life prior to expiry date) and different PCR machines (two systems with different ages but
same brand). The samples used were bovine, porcine or chicken-fortified feed or food at 5 or
more concentration levels within the quantitative ranges of the assays (S2 Table). The effect of
DNA degradation on ddPCR readings was tested using raw and cooked beef DNA after 3
different degradation treatments, Taq I digestion, sonication (42 KHZ) for 2 min and sonication
(42 KHZ) for 10 min using a Branson 1510 sonicator (Fisher Scientific, Ottawa, ON). The
ddPCR output was compared to the readings from DNA without degradation treatment.
Statistical analyses were performed using chi-square test or paired sample t-test with SAS
9.4 software program to reveal quantification variation significance against different matrices
6 / 17
and experimental parameters. GraphPad Prism 6 was used to determine linearity and draw
Specificity of the primers and probes was tested first in silico using the Nucleotide Basic Local
Alignment Search Tool (BLAST, http://blast.ncbi.nlm.nih.gov/Blast.cgi). The specificity of the
ddPCR assays, including the internal control ddPCR assay, was further tested experimentally
using DNA from target and non-target species samples. The ddPCR assays yielded positive
results when target DNA was tested using the corresponding primers/probe while non-target
DNA was negative, indicating no cross amplification. The results confirmed the specificity of
the ddPCR assays for selective detection of bovine, porcine, chicken and turkey species
(Table 2) as well as the internal control within its specificity range (Table 1).
Linearity and limit/range of quantification
The linearity of the optimized ddPCR assays was determined using purified DNA from fresh
meat tissue at concentrations of 0.26±532 pg/PCR. The linear regression was established
within the concentrations of 1.3±106.8 pg/PCR for beef and chicken, 2.2±176.0 pg/PCR for
pork, and 0.26±64 pg/PCR for turkey. These ranges of concentrations were equivalent to 79±
33200 copies/PCR, with coefficients of determination (R2) ranging from 0.997±0.999
(pvalue < 0.0001) (Fig 1).
Limit and range of quantification of the complete analytical processes were determined
based on data from fortified heat-processed food/feed samples at different concentrations
(0.005% to 10.00%, wt/wt) of beef in poultry meal, pork in chicken, chicken in pork or turkey
in pork. The quantification range was found to be 0.05±3.00% (wt/wt) for heat-processed
beef and turkey and 0.01±1.0% (wt/wt) for heat-processed pork and chicken with the
coefficient of determination (R2) of 0.979±0.998 (p-value < 0.0001 for beef, pork and turkey, and
pvalue = 0.003 for chicken) (Fig 2). When the spiking levels were higher than 1.0 or 3.0% (wt/
wt), the target molecule copies per droplet were above 1.5 (or 30000 copies/PCR), and the
curves exhibited a plateau shape. This level was close to the theoretical upper limit of the
ddPCR platform for quantification [
S2 Fig represents an example illustrating how the range of quantification of the bovine
ddPCR assay was determined. For spiking levels above 3% the curve was no longer linear (S2A
Fig) while it was linear for spiking levels up to 3% (S2B Fig). For spiking levels below 0.05%,
the assay was not able to differentiate among 0%, 0.005% and 0.01% (S2C Fig).
Repeatability and reproducibility
The ddPCR methods were tested for repeatability using replicated DNA extractions, PCRs and
by using fortified heat-processed meat or feed samples. With few exceptions, the relative
standard deviations (RSD) calculated for repeatability between PCR replicates and between DNA
extractions were below 20% (Fig 3). The reproducibility of the methods was determined using
three independent experiments conducted on different days and by different operators and
was found to be below 20% of RSD with few exceptions (Fig 3). The higher RSD % observed in
a few cases may have been caused mainly by variation in sample preparation. For example, the
higher RSD at 0.1% for the bovine target (Fig 3) may have been caused by the heterogeneous
nature of the feed matrix which contained small bone particles. The reproducibility of ddPCR
was further monitored over time by making a daily control chart using the bovine ddPCR as
7 / 17
Fig 1. Linear regression between ddPCR output (copies/PCR) and DNA amount (ng). DNA was extracted from fresh raw (A) beef, (B) pork, (C)
chicken, and (D) turkey. Shown are results from two replicates in green and blue colors.
an example. Fig 4 shows the ddPCR output of a fortified bovine positive control sample tested
in 74 consecutive runs with each of the runs being performed on a different day. The variations
(RSD = 8.6%) were within the reported technical range of 20% for the ddPCR platform (Fig 4).
Matrix effect was evaluated using a heat-processed animal tissue sample spiked into food or
feed matrices. A matrix effect was observed in some of the fortified samples. For example,
there was an over 45% difference in ddPCR output when spiking chicken into beef and pork
salami as compared to chicken spiked into beef hot dogs (Fig 5).
The ddPCR assays were evaluated using DNA prepared from fortified samples of beef, pork
and chicken within their quantification ranges under different DNA storage conditions,
prolonged PCR reagent shelf life and using different PCR machines. RSD (%) ranges obtained
under the different conditions were 2.0±22.6, 1.5±14.9 and 1.7±13.1 for the bovine, porcine
8 / 17
Fig 2. Linear regression between ddPCR output (copies/PCR) and target animal species concentration (% wt/wt) in fortified
heatprocessed samples. (A) beef in poultry meal, (B) pork in chicken, (C) chicken in pork, and (D) turkey in pork. Shown are results from four replicates
in green, blue, purple and black.
and chicken ddPCR assays respectively (S2 Table). The RSD (%) values were within the
acceptable range of quantitative accuracy, indicating that the assays can be performed using either
fresh or frozen DNA, either fresh or old (close to the end of shelf life) reagent and using
different PCR machines without compromising the results.
Effect of DNA degradation on ddPCR readings
The effect of DNA degradation on ddPCR output was tested on both raw and cooked beef
DNA after 3 different degradation treatments: Taq I restriction enzyme digestion, 2 min
sonication, and 10 min sonication. The ddPCR output from degradation-treated DNA was
compared to untreated DNA. Taq I digestion was found to significantly reduce ddPCR output for
both raw and cooked beef DNA. Sonication for 10 min resulted in over 4.5 fold reduction in
ddPCR output for raw beef DNA, and approximately 20% reduction for cooked beef DNA
while sonication for 2 min showed little effect on ddPCR output for either raw or cooked meat
DNA (Table 3). In addition, heat treatment of the beef tissue sample under the commonly
used autoclave condition (15 min at 121ÊC) also resulted in over 3 fold reduction in the
ddPCR output (Table 3).
9 / 17
Fig 3. Repeatability and reproducibility of the ddPCR methods. Relative standard deviations (RSD%) are presented for fortified
heat-processed (A) beef in poultry meal, (B) pork in chicken, (C) chicken in pork and (D) turkey in pork. Lines represent average RSD
% for repeatability between DNA extractions and reproducibility between different operators. The data points cover all five spiking
levels as labeled for each of the target species tested.
Result calculation and interpretation
The ddPCR provides an absolute quantification of target DNA without relying on a standard
curve. Specifically, the ddPCR output is in copies of input DNA. The correlation between the
amount of DNA from fresh tissue and their copy numbers from the ddPCR was y = 291235x
+103.18 for bovine, y = 188491x + 205.78 for porcine, y = 181118x−467.71 for chicken, and
y = 398422x−240.49 for turkey (where y is the copy number and x is ng of DNA) (Fig 1). Based
on these correlations, DNA mass of an amplified target from a sample can be calculated from
its copy number. For example, the 3.0% (wt/wt) heat-treated beef in poultry meal feed resulted
in 27000 copies which is equivalent to 0.0924 ng bovine DNA, or 0.23% in DNA mass/DNA
mass. The quantitative range of 0.05%±3.00% (wt/wt) for the bovine target is equivalent to
0.0025±0.23% (DNA mass/DNA mass). When the % value is lower than the LOQ (0.0025%
DNA mass or 0.05% dry weight of the sample), the result is reported to be <LOQ; when the %
10 / 17
Fig 4. Internal validation of ddPCR for quantification of bovine DNA in a fortified poultry meal feed
sample. The results were obtained from 74 consecutive runs conducted on different days. The variation (RSD
%) was 8.6.
Fig 5. An example showing the effect of sample matrix on ddPCR output (copies/PCR). A cooked chicken
sample was spiked in pork summer sausage, beef hot dog or beef and pork salami matrices.
(0.032 ng DNA in AE Buffer)
Raw beef DNA
Cooked beef DNA
11 / 17
Copies/PCR upon different treatments
Taq I digestion
2 min sonication
10 min sonication
value is higher than 0.23% DNA mass or 3.00% dry weight, the DNA sample needs to be
diluted to the quantitative range and retested.
The IC readings were used to normalize PCR outputs affected by variabilities in the PCR
procedures. However, normalization of the ddPCR outputs to the IC readings did not affect
the result if the IC readings were within 20% variation as compared to the expected values. S3
Fig illustrates an example of normalized and un-normalized results from the bovine ddPCR
assay, which resulted in a p-value of 0.86 (n = 50) from paired t-test.
Food authentication continues to be of interest in an era of globalization as more reports and
studies demonstrate a high incidence of adulteration and/or mislabeling. Here we describe
and validate ddPCR- based assays for quantitative analysis of bovine, porcine, chicken and
turkey DNA in food and feed. The ddPCR assays were evaluated systematically for their
specificity, limit/range of quantification, repeatability and reproducibility, matrix effect and
robustness. Other researchers have reported ddPCR assays for meat species quantification
previously, including ddPCR assays for quantifying pork and chicken species [
], and for testing
beef, pork and horse in meat products [
]. In this paper, we extended the detection scope to
include beef, pork, chicken and turkey, investigated impact of sample nature and processing
on results and enhanced the methods by integrating an internal control into the ddPCR assays
to ensure data reliability.
The ddPCR assays described here were demonstrated to be specific upon testing 10±11
samples containing the target species and 45±46 samples belonging to 18 non-target species.
The linear quantification range of these methods was 0.26±176 pg/PCR for fresh meat tissue
DNA and 0.01±1.0% (wt/wt) for porcine and chicken ddPCR, and 0.05±3.0% (wt/wt) for
bovine and turkey ddPCR for fortified heat-processed food and feed. Floren et al [
reported a ddPCR limit of quantification of 0.01% for mixed meat products. It is advised that
the limits of quantification may not be comparable since meat samples were prepared by
autoclaving for 15 minutes at 121ÊC in this study while cooking conditions to prepare the
meat samples were not provided in the previous publication [
]. When the target species levels
were above 1.0 or 3.0% (wt/wt), the linear regression curves exhibited a plateau. This result
was expected as this level is considered close to the theoretical upper limit (30000 droplets) of
the ddPCR platform for quantification. The dynamic quantification range for ddPCR is
narrower than that of qPCR. However, dilution of the DNA preparations will allow the methods
to quantify the target species above the upper limit of 1.0 or 3.0%. For example, representative
samples containing 10.0% of bovine species were diluted and tested; ddPCR outputs
proportional to the dilutions were observed (results not shown). Similar dilution practice was also
reported by other researchers previously [
]. The repeatability and reproducibility of these
methods were within 20% of RSD in general. Overall, the results indicate that the ddPCR
assays can be used to reliably generate quantification results of the target DNA, especially for
monitoring partial species substitution, and cross-contamination against pre-determined
The target sequences used for meat species detection in this study were mitochondrial
DNA (mtDNA), which are widely used in animal species detection in complex food and feed
]. The mitochondrial DNA genes were selected as the ddPCR targets in this study
due to their presence in high copy numbers in animal species, achieving higher sensitivity and
facilitating detection of trace quantities in food and feed matrices, especially after processing
and severe DNA degradation [
]. The effect of tissue type on ddPCR output was
investigated in this study. We found that fresh bovine heart and pig liver resulted in 1.35 and 3 fold
12 / 17
more target copies per unit weight of DNA in the bovine and porcine ddPCR respectively as
compared to the fresh muscle samples. The effect of tissue type on ddPCR output is likely
caused by the fact that the number of mitochondria per cell varies with tissue type [
As reported previously, liver cells were found to contain approximately 3 times more
mitochondria than muscle cells [
]. An alternative would be to use single copy genes (SCG) as
amplification targets [
]. However, assays developed based on SCG may suffer from reduced
sensitivity while high sensitivity is desired for applications where products are deeply
processed and low tolerance is expected, such as testing for traces of bovine in feed or pork in
Halal food, or monitoring product cross-contamination during production.
The ddPCR assays developed in this study contained an internal control (IC) to ensure
reliability of the results, to normalize variabilities in the PCR procedures and to safeguard against
false negatives due to factors such as PCR inhibition or reaction failure. The internal control
plasmid DNA was added at a level to generate approximately 1700±20% copies per ddPCR
reaction. At this IC concentration, no amplification competition was observed between the IC
and the target DNA. Competitive amplification between the detection target and the IC was
observed in ddPCR when more copies of IC were used and the target copy number was lower
than that of IC in a reaction (S1 Table). The PCR test will pass the quality control only if the
internal control results in the expected output with a 20% variation. Internal controls are
used or required for qPCR assays [
] while the use of an internal control in ddPCR for meat
species detection has not been a common practice in previous studies [
]. The internal
control created in this study can also be used in the format of recombinant E. coli cells. The IC
cells can be added to food or feed samples, co-extracted and then co-amplified with the target,
not only for monitoring inhibition or failure of amplification, but also for monitoring DNA
extraction efficiency and calculating recovery.
Matrix effect was observed when testing fortified samples, or heat-processed animal tissue
spiked into different food and feed sample matrices, such as sausages and hot dogs. However,
the ddPCR output remained in linear correlation with target concentration. The matrix effect
observed here may be explained by different components in the samples such as fats, presence
of microbial population and also by the heterogeneous nature of the food and feed samples.
Systematic matrix effect can be corrected by creating calibration curves or normalizing the
data to the internal control when the IC is included in the sample before DNA extraction.
Validation must be conducted in order to accurately quantify a target species in a matrix of
It is desirable to use ªreference materialsº in a validation study that are prepared under
conditions close to production of the food or feed under testing. Meat materials were prepared
inhouse in this study due to lack of available reference materials representative of typical
industrial meat and feed processing. Variations are expected from fortified materials prepared in
different labs. Certified reference materials are needed to ascertain comparable results among
different methods or different labs. These reference materials will help overcome limitations
from using different food and feed materials in validation studies and extend the ability of
ddPCR for use as a reliable quantitative technique, facilitating the establishment of consensus
methods for food and feed testing. They will also ensure equivalency of results and support
laboratory proficiency testing needs.
Food and feed production processes can cause severe DNA degradation due to heat or
physical damage, resulting in several fold reduction in ddPCR outputs as compared to those
from fresh tissues, as observed in this study. The effect of DNA degradation on ddPCR results
was also observed with procedures that may be used in sample analysis, such as heating and
sonication of a sample and restriction enzyme digestion of a DNA template. We found that
heat treatment of a beef tissue sample by autoclaving for 15 min resulted in over 3 fold
13 / 17
reduction in the ddPCR readings. Prolonged (e.g. 10 min) sonication of the template DNA
resulted in underestimation (e.g. 4.5 fold reduction) of the target. Restriction enzyme digestion
of genomic DNA templates has been recommended in the QX 200 experiment protocol for
ddPCR in the manufacturer's manual; however, Taq I digestion of the template in this study
was found to significantly reduce ddPCR output although there was no Taq I restriction site
within the amplicon. The prolonged incubation of the template during Taq I digestion (65ÊC
for 1 hr) may have resulted in degradation within the amplicon. These findings emphasize the
importance of minimising DNA degradation in analytical processes to ensure that the
quantification numbers reflect the true nature of the samples. As shown, food processing can
potentially result in underestimation of target species.
The ddPCR assays described in this report met the accepted performance criteria of the
ddPCR platform. The advantages of the methods include their high sensitivity, and ability to
reliably quantify low concentration of DNA in a high background DNA without using
standard curves. The internal control developed in this study can be used to monitor the PCR
procedures and is recommended to be included in ddPCR assays to assess recovery and correct
matrix effect. The methods can be used for quantitative analysis of bovine, porcine, chicken
and turkey DNA in food and feed in validated matrices, particularly for products that are
deeply processed or degraded and in which trace amount of foreign meat species is not
tolerated. Standard reference material should be developed in collaboration with industry to mirror
common production processes. The ddPCR methods can be implemented in routine testing to
identify food fraud and to monitor the prohibited animal species in feed chain with enhanced
sensitivity, accuracy and precision without reliance on standard curves.
S1 Table. Optimizing the concentration of internal control used in ddPCR assays.
S2 Table. Results of robustness study.
S1 Fig. Evaluating the repeatability of the ddPCR assay for the internal control (IC). IC
was tested 16 times. The RSD% was 5.53.
S2 Fig. Example illustrating how the limit/range of quantification of the bovine ddPCR
assay was determined. (A) ddPCR results for fortified beef in poultry meal at 0, 0.005, 0.01,
0.05, 0.1, 0.5, 1.0, 3.0, 5.0, and 10.0% (wt/wt). The curve exhibited a plateau when beef content
was over 3.0%. (B-C) are subset data of (A) where (B) shows beef in poultry meal at 0, 0.005,
0.01, 0.05, 0.1, 0.5, 1.0, and 3.0% (wt/wt). After removing the 5.0 and 10.0% data points, the
curve was linear. The upper limit was thus determined to be 3.0%. (C) shows beef in poultry
meal at 0, 0.005, 0.01, 0.05, and 0.1% (wt/wt). The assay was unable to differentiate among 0,
0.005, and 0.01% of beef. The lower limit was thus determined to be 0.05%. The linear
relationship was established between 0.05 and 3.0% beef as shown in Fig 2A. Each concentration was
tested in 12 replicates.
S3 Fig. Evaluating the effect of normalizing the ddPCR output using the internal control
(IC). ddPCR results were obtained from testing fortified heat-processed beef in poultry meal
14 / 17
without normalization to IC (A) and after normalization to IC (B).
Conceptualization: Jiping Li, Shu Chen.
Data curation: Jiping Li, Shu Chen.
Formal analysis: Hanan R. Shehata, Jiping Li, Shu Chen.
Funding acquisition: Shu Chen, Robert Hanner.
Investigation: Jiping Li, Shu Chen, Helen Redda, Shumei Cheng, Nicole Tabujara, Honghong
Methodology: Jiping Li, Shu Chen.
Project administration: Shu Chen.
Resources: Shu Chen.
Supervision: Jiping Li, Shu Chen, Keith Warriner, Robert Hanner.
Validation: Hanan R. Shehata, Jiping Li, Shu Chen, Helen Redda, Shumei Cheng, Nicole
Tabujara, Honghong Li.
Visualization: Hanan R. Shehata.
Writing ± original draft: Hanan R. Shehata, Jiping Li, Shu Chen.
Writing ± review & editing: Hanan R. Shehata, Shu Chen, Nicole Tabujara, Keith Warriner,
15 / 17
16 / 17
1. Spink J , Moyer DC . Defining the public health threat of food fraud . J Food Sci . 2011 ; 76 ( 9):R157±R63 . https://doi.org/10.1111/j.1750- 3841 . 2011 . 02417 . x PMID : 22416717
2. Chuah L-O , He XB , Effarizah ME , Syahariza ZA , Shamila-Syuhada AK , Rusul G . Mislabelling of beef and poultry products sold in Malaysia . Food Control . 2016 ; 62 : 157 ± 64 .
3. Zacharisen MC . Severe allergy to chicken meat . Wis Med J. 2006 ; 105 ( 5 ): 50 ± 2 .
4. Floren C , Wiedemann I , Brenig B , Schutz E , Beck J . Species identification and quantification in meat and meat products using droplet digital PCR (ddPCR) . Food Chem . 2015 ; 173 : 1054 ±8. https://doi.org/ 10.1016/j.foodchem. 2014 . 10 .138 PMID: 25466124
5. O 'Mahony PJ . Finding horse meat in beef productsÐa global problem . QJM . 2013 ; 106 ( 6 ): 595 ±7. https://doi.org/10.1093/qjmed/hct087 PMID: 23625529
6. Di Pinto A , Bottaro M , Bonerba E , Bozzo G , Ceci E , Marchetti P , et al. Occurrence of mislabeling in meat products using DNA-based assay . J Food Sci Technol . 2015 ; 52 ( 4 ): 2479 ± 84 . https://doi.org/10. 1007/s13197-014-1552-y PMID: 25829637
7. Bottaro M , Marchetti P , Mottola A , Shehu F , Di Pinto A. Detection of mislabeling in packaged chicken sausages by PCR . Albanian J Agric Sci . 2014 : 455 .
8. Tartaglia M , Saulle E , Pestalozza S , Morelli L , Antonucci G , Battaglia PA . Detection of bovine mitochondrial DNA in ruminant feeds: a molecular approach to test for the presence of bovine-derived materials . J Food Prot . 1998 ; 61 ( 5 ): 513 ± 8 . PMID: 9709219
9. Kumar A , Kumar RR , Sharma BD , Gokulakrishnan P , Mendiratta SK , Sharma D . Identification of species origin of meat and meat products on the DNA basis: a review . Crit Rev Food Sci Nutr . 2015 ; 55 ( 10 ): 1340 ± 51 . https://doi.org/10.1080/10408398. 2012 .693978 PMID: 24915324
10. Sentandreu M AÂ , Sentandreu E. Authenticity of meat products: Tools against fraud . Food Res Int . 2014 ; 60 : 19 ± 29 .
11. Ali ME , Kashif M , Uddin K , Hashim U , Mustafa S , Che Man YB . Species authentication methods in foods and feeds: the present, past, and future of halal forensics . Food Anal Method . 2012 ; 5 ( 5 ): 935 ± 55 .
12. Ansfield M , Reaney S , Jackman R . Production of a sensitive immunoassay for detection of ruminant and porcine proteins, heated to >> 130ÊC at 2.7 bar, in compound animal feedstuffs . Food Agric Immunol . 2000 ; 12 ( 4 ): 273 ± 84 .
13. Haider N , Nabulsi I , Al-Safadi B . Identification of meat species by PCR-RFLP of the mitochondrial COI gene . Meat Sci . 2012 ; 90 ( 2 ): 490 ±3. https://doi.org/10.1016/j.meatsci. 2011 . 09 .013 PMID: 21996288
14. Ali ME , Razzak MA , Hamid SBA . Multiplex PCR in species authentication: probability and prospectsÐA review . Food Anal Method . 2014 ; 7 ( 10 ): 1933 ± 49 .
15. Mafra I , Ferreira IMPLVO , Oliveira MBPP . Food authentication by PCR-based methods . Eur Food Res Technol . 2008 ; 227 ( 3 ): 649 ± 65 .
16. Calvo JH , Rodellar C , Zaragoza P , Osta R . Beef-and bovine-derived material identification in processed and unprocessed food and feed by PCR amplification . J Agric Food Chem . 2002 ; 50 ( 19 ): 5262 ± 4 . PMID: 12207458
17. Ekins J , Peters SM , Jones YL , Swaim H , Ha T , La Neve F , et al. Development of a multiplex real-time PCR assay for the detection of ruminant DNA . J Food Prot . 2012 ; 75 ( 6 ): 1107 ± 12 . https://doi.org/10. 4315/ 0362 - 028X . JFP-11-415 PMID: 22691479
18. Frezza D , Favaro M , Vaccari G , Von-Holst C , Giambra V , Anklam E , et al. A competitive polymerase chain reaction±based approach for the identification and semiquantification of mitochondrial DNA in differently heat-treated bovine meat and bone meal . J Food Prot . 2003 ; 66 ( 1 ): 103 ± 9 . PMID: 12540188
19. KrčmaÂř P , RenčovaÂ E. Identification of bovine-specific DNA in feedstuffs . J Food Prot . 2001 ; 64 ( 1 ): 117 ± 9 . PMID: 11198432
20. Prado M , Berben G , Fumière O , van Duijn G , Mensinga-Kruize J , Reaney S , et al. Detection of ruminant meat and bone meals in animal feed by real-time polymerase chain reaction: result of an interlaboratory study . J Agric Food Chem . 2007 ; 55 ( 18 ): 7495 ± 501 . https://doi.org/10.1021/jf0707583 PMID: 17725317
21. RodrÂõguez MA , GarcÂõa T , GonzaÂlez I , Asensio L , HernaÂndez PE , MartÂõn R. PCR identification of beef, sheep, goat, and pork in raw and heat-treated meat mixtures . J Food Prot . 2004 ; 67 ( 1 ): 172 ± 7 . PMID: 14717369
22. Zhang C-L , Fowler MR , Scott NW , Lawson G , Slater A . A TaqMan real-time PCR system for the identification and quantification of bovine DNA in meats, milks and cheeses . Food Control . 2007 ; 18 ( 9 ): 1149 ± 58 .
23. Huggett JF , Foy CA , Benes V , Emslie K , Garson JA , Haynes R , et al. Guidelines for minimum information for publication of quantitative digital PCR experiments . Clin Chem . 2013 ; 59 ( 6 ): 892 ± 902 .
24. Cai YC , Li X , Lv R , Yang JL , Li J , He YP , et al. Quantitative analysis of pork and chicken products by droplet digital PCR . BioMed Res Int . 2014 ; 2014 ( 1):1±6 .
25. Baker M. Digital PCR hits its stride . Nat Meth . 2012 ; 9 ( 6 ): 541 ± 4 .
26. Hindson BJ , Ness KD , Masquelier DA , Belgrader P , Heredia NJ , Makarewicz AJ , et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number . Anal Chem . 2011 ; 83 ( 22 ): 8604 ± 10 . https://doi.org/10.1021/ac202028g PMID: 22035192
27. Dingle TC , Sedlak RH , Cook L , Jerome KR . Tolerance of droplet-digital PCR vs real-time quantitative PCR to inhibitory substances . Clin Chem . 2013 ; 59 ( 11 ): 1670 ±2. https://doi.org/10.1373/clinchem. 2013 . 211045 PMID: 24003063
28. Nadauld L , Regan JF , Miotke L , Pai RK , Longacre TA , Kwok SS , et al. Quantitative and sensitive detection of cancer genome amplifications from formalin fixed paraffin embedded tumors with droplet digital PCR . Translational medicine (Sunnyvale, Calif) . 2012 ; 2 ( 2 ):1± 12 .
29. Henrich TJ , Gallien S , Li JZ , Pereyra F , Kuritzkes DR . Low-level detection and quantitation of cellular HIV-1 DNA and 2-LTR circles using droplet digital PCR . J Virol Methods . 2012 ; 186 ( 1 ): 68 ± 72 .
30. Kelley K , Cosman A , Belgrader P , Chapman B , Sullivan DC . Detection of methicillin-resistant Staphylococcus aureus by a duplex droplet digital PCR assay . J Clin Microbiol . 2013 ; 51 ( 7 ): 2033 ± 9 . https://doi. org/10.1128/JCM.00196-13 PMID: 23596244
31. Rothrock MJ , Hiett KL , Kiepper BH , Ingram K , Hinton A . Quantification of zoonotic bacterial pathogens within commercial poultry processing water samples using droplet digital PCR . Advances in Microbiology. 2013 ; 3 ( 05 ): 403 .
32. Strain MC , Lada SM , Luong T , Rought SE , Gianella S , Terry VH , et al. Highly precise measurement of HIV DNA by droplet digital PCR . PloS one . 2013 ; 8 ( 4 ):e55943. https://doi.org/10.1371/journal.pone. 0055943 PMID: 23573183
33. Morisset D , SÏ tebih D , Milavec M , Gruden K , ZÏ el J. Quantitative analysis of food and feed samples with droplet digital PCR . PloS one . 2013 ; 8 ( 5 ):e62583. https://doi.org/10.1371/journal.pone. 0062583 PMID: 23658750
34. ANSI. Species-Level Identification of Animal Cells through Mitochondrial Cytochrome c Oxidase Subunit 1 (CO1) DNA Barcodes 2015 [ cited 2017] . ANSI/ATCC ASN-0003-2015:[http://webstore.ansi.org/ RecordDetail.aspx?sku=ANSI%2FATCC+ ASN -0003- 2015 .
35. Udomsil N , Chen S , Rodtong S , Yongsawatdigul J . Quantification of viable bacterial starter cultures of Virgibacillus sp . and Tetragenococcus halophilus in fish sauce fermentation by real-time quantitative PCR . Food Microbiol . 2016 ; 57 : 54 ± 62 . https://doi.org/10.1016/j.fm. 2016 . 01 .004 PMID: 27052702
36. Tanabe S , Hase M , Yano T , Sato M , Fujimura T , Akiyama H. A real-time quantitative PCR detection method for pork, chicken, beef, mutton, and horseflesh in foods . Biosci Biotechnol Biochem . 2007 ; 71 ( 12 ): 3131 ±5. https://doi.org/10.1271/bbb.70683 PMID: 18071237
37. Krcmar P , Rencova E . Identification of species-specific DNA in feedstuffs . J Agric Food Chem . 2003 ; 51 ( 26 ): 7655 ±8. https://doi.org/10.1021/jf034167y PMID: 14664524
38. Abuzinadah OH , Yacoub HA , El Ashmaoui HM , Ramadan HA . Molecular detection of adulteration in chicken products based on mitochondrial 12S rRNA gene . Mitochondrial DNA . 2015 ; 26 ( 3 ): 337 ± 40 . https://doi.org/10.3109/19401736. 2013 .840593 PMID: 24102598
39. AOAC983 .18. Moisture in meat . AOAC Method 2008 ; 950 . 46B ( 1991 revision).
40. AOAC. Meat and meat products-Preparation of test sample procedures . 2006 .
AOAC. AOAC Method 1983 . 983 .18.
42. Pinheiro LB , Coleman VA , Hindson CM , Herrmann J , Hindson BJ , Bhat S , et al. Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification . Anal Chem . 2012 ; 84 ( 2 ): 1003 ± 11 . https://doi.org/10.1021/ac202578x PMID: 22122760
43. Ballin NZ , Vogensen FK , Karlsson AH . Species determinationÐCan we detect and quantify meat adulteration? Meat Sci . 2009 ; 83 ( 2 ): 165 ± 74 . https://doi.org/10.1016/j.meatsci. 2009 . 06 .003 PMID: 20416768
44. RodrÂõguez MA , GarcÂõa T , GonzaÂlez I , Asensio L , Mayoral B , LoÂpez-Calleja I , et al. Identification of goose, mule duck, chicken, turkey, and swine in foie gras by species-specific polymerase chain reaction . J Agric Food Chem . 2003 ; 51 ( 6 ): 1524 ±9. https://doi.org/10.1021/jf025784+ PMID: 12617577
45. Diez-Valcarce M , Cook N , HernaÂndez M , RodrÂõguez-LaÂzaro D. Analytical application of a sample process control in detection of foodborne viruses . Food Anal Method . 2011 ; 4 ( 4 ): 614 ± 8 .