INTRODUCTION
The Russian Federation strategy to improve the
quality of food products until 2030 prioritizes research
in the field of quality management.
Today, the problem of food adulteration is of
particular concern. Food manufacturers are increasingly
replacing expensive raw materials, such as good quality
beef, with cheaper poultry. According to the public
report “Consumer Protection in the Russian Federation
in 2017”, Rospotrebnadzor (Russian Federal Service for
Surveillance on Consumer Rights Protection and Human
Wellbeing) detected 3410 adulterated products out of
310 000 inspected food samples [1]. In 2018, the volumes
of rejected meat, poultry, and their products doubled
compared to 2017. In particular, Rospotrebnadzor
rejected 519 batches of meat and meat products
weighing 3509 kg (compared to 459 batches of
1685 kg in 2017) and 168 batches of poultry, eggs,
and their products weighing 1951 kg (compared to
159 batches of 975 kg in 2017).
Research Article DOI: http://doi.org/10.21603/2308-4057-2020-1-98-106
Open Access Available online at http://jfrm.ru/en/
Methodology for identification and quantification
of chicken meat in food products
Mariya A. Pleskacheva1 , Marina P. Artamonova2 , Elena V. Litvinova2, * ,
Mariia A. Gergel1 , Ekaterina E. Davydova3
1 The Russian State Center for Animal Feed and Drug Standardization and Quality, Moscow, Russia
2 Moscow State University of Food Productions, Moscow, Russia
3 The Center for Strategic Planning and Management of Medical and Biological Health Risks, Moscow, Russia
* e-mail: illusionse@mail.ru
Received December 20, 2019; Accepted in revised form January 13, 2020; Published March 31, 2020
Abstract:
Introduction. The problem of food adulteration is highly relevant today. Food manufacturers are increasingly replacing expensive raw
materials with cheaper poultry. We aimed to develop an effective method for identification and quantification of chicken meat and egg
products in multicomponent meat systems using real-time PCR.
Study objects and methods. We studied native animal tissue, namely that of chicken, pork, beef, turkey, quail, duck, horse meat, rabbit,
sheep, and goat. Standard samples were taken from pure fresh chicken muscle tissue. We also used raw, boiled, and powdered chicken
eggs. For a semiquantitative analysis of chicken mass in the sample, we compared the threshold cycle (Сt) of chicken DNA and the
threshold cycles of calibration samples. To ensure the absence of PCR inhibition, we used an internal control sample which went
through all the stages of analysis, starting with DNA extraction.
Results and discussion. We developed a methodology to qualitatively determine the content of chicken tissue in the product
and distinguish between the presence of egg products and contamination on the production line. The method for chicken DNA
identification showed 100% specificity. This genetic material was detected in the range of 0.1% to 0.01% of chicken meat in the
sample. The efficiency of the duplex PCR system for chicken DNA detection was more than 95% (3.38 on the Green slope channel
and 3.45 on the Yellow slope channel). The analytical sensitivity of the primers was 40 copies/reaction.
Conclusion. Our methodology is suitable for analyzing multicomponent food products, raw materials, feed, and feed additives.
It can identify the content of chicken meat at a concentration of up to 1%, as well as distinguish egg impurities from contamination
of various origin. PCR allows differentiation between chicken meat and egg products.
Keywords: Multicomponent products, canned food, chicken meat, egg melange, PCR, adulteration, sausages
Please cite this article in press as: Pleskacheva MA, Artamonova MP, Litvinova EV, Gergel MA, Davydova EE. Methodology for
identification and quantification of chicken meat in food products. Foods and Raw Materials. 2020;8(1):98–106. DOI: http://doi.
org/10.21603/2308-4057-2020-1-98-106.
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Species identification of meat and meat products is
becoming more important due to increased international
trade and labeling rules introduced in many countries.
Morphological and anatomical characteristics are
used to identify fresh and unprocessed meat. However,
processed meat loses its characteristic morphological
features, which creates favorable conditions for
adulteration, namely for replacing one type of meat
with another, less valuable type. Poultry – a cheaper
raw material compared to pork, beef or other meats –
is often used to adulterate products, both semi-finished
and finished. Especially difficult is species identification
of multicomponent products containing several types of
meat, egg impurities, various food additives, enzyme
preparations, as well as products subjected to rigorous
mechanical or thermal processing, such as canned
foods and pastes [2–7]. According to Rospotrebnadzor,
most violations of the technical standards in 2018 were
detected in canned meat and sausages [1].
At the moment, the Russian Federation has no
method for quantifying the content of chicken and/
or egg melange in food products and isolating possible
contamination on the production line.
Scientific literature reports numerous methods for
qualitative identification of meat species [8–11]I. A group
of scientists from Gorbatov’s Federal Scientific Center
for Food Systems and the National Center for Fishing
Products Safety attempted to identify egg melange at the
30th PCR cycle [12, 13]. However, there were no data
on the quantitative identification of impurities [14, 15].
Therefore, we need to develop a quantitative method
for identifying ingredients in the analyzed products to
prevent producers from replacing a specified content
of meat with cheaper raw materials and to distinguish
between adulteration and inevitable contamination in
production [16–20].
The highly sensitive PCR method can reveal even
trace amounts of meat ingredients, which are essentially
technical impurities. However, in order to distinguish a
minor technical impurity from intentional adulteration,
we need a methodology for a quantitative or semiquantitative
evaluation of meat, for example, chicken, in
food products [20–38].
Therefore, we aimed to develop an effective method
for identification and quantification of chicken meat and
egg products in multicomponent meat systems using the
real-time PCR.
STUDY OBJECTS AND METHODS
Our objects of study included native animal tissue
purchased in retail chain stores (chicken, pork, beef,
turkey, quail, duck, horse meat, rabbit, sheep, and goat)
or obtained at the Russian State Center for Animal
Feed and Drug Standardization and Quality, Moscow
I MU А 1/022 Sekvenirovanie fragmentov mitokhondrialʹnogo
genoma zhivotnykh i ryb dlya opredeleniya vidovoy prinadlezhnosti
myasa v odnokomponentnoy produktsii [MU А 1/022 Sequencing
fragments of the mitochondrial genome of animals and fish to
determine meat species in mono-component products].
(mink, cat, and dog). Pure fresh chicken muscle tissue
was used as standard samples. The species identity
of all the materials was confirmed by the Sanger
DNA sequencing method based on the standard CytB
gene [3]. In addition, we used raw, boiled, and powdered
chicken eggs.
We used only certified equipment, materials,
reagents, and utensils.
The tests were conducted using the following
methods:
– taking laboratory samples from different product
groups (State Standard 31904-2012II);
– adsorption DNA extraction based on silicon dioxide
(State Standard R 56140-2014III);
– guanidine-chloroform-based DNA extraction (State
Standard R ISO 21571-2014IV). This method can purify
DNA from fatty and protein impurities, reduce the
inhibition of the reaction, and eliminate the influence of
food additives on the final result (it also works well with
egg impurities);
– real-time polymerase chain reaction with
hybridization-fluorescence detection (State Standard
ISO 22119-2013V);
– evaluation of metrological characteristics of measurement
procedures (RIS 61-2010VI);
– certification of measurement procedures (State
Standard R 8.563-2009VII).
When sampling and preparing test samples, we
took measures to prevent the seeding of environmental
objects in line with State Standard 8756.0-70VIII
and State Standard 31719-2012IX. The samples were
homogenized and 0.05 g weighed, placed in a 1.5 cm
Eppendorf type disposable microcentrifuge tube,
labeled, and used to isolate DNA.
Three sets of samples were prepared in duplicate.
The first set was not subjected to heat treatment. The
samples of the second set were mixed with 100 mm3
II State Standard 31904-2012. Food products. Methods of sampling for
microbiological analyses. Moscow: Standartinform; 2014. 8 p.
III State Standard R 56140-2014. Medicine biological remedies for
veterinary use. Polymerase chain reaction for the Mycoplasma DNA
detection. Moscow: Standartinform; 2015. 12 p.
IV State Standard R ISO 21571-2014. Foodstuffs. Methods of analysis
for the detection of genetically modified organisms and derived
products. Nucleic acid extraction. Moscow: Standartinform; 2016. 46 p.
V State Standard ISO 22119-2013. Microbiology of food and animal
feeding stuffs. Real-time polymerase chain reaction (PCR) for
the detection of food-borne pathogens. General requirements and
definitions. Moscow: Standartinform; 2014. 15 p.
VI RIS 61-2010. State system for ensuring the uniformity of
measurements. Accuracy, trueness and precision measures of the
procedures for quantitative chemical analysis. Methods of evaluation.
Moscow: Standartinform; 2013. 62 p.
VII State Standard R 8.563-2009. State system for ensuring the
uniformity of measurements. Procedures of measurements. Moscow:
Standartinform; 2011. 20 p.
VIII State Standard 8756.0-70. Canned food products. Sampling and
preparation of samples for test. Moscow: Standartinform; 2010. 8 p.
IX State Standard 31719-2012. Foodstuffs and feed. Rapid method
of identification of raw composition (molecular). Moscow:
Standartinform; 2014. 24 p.
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of water and heated at 99°С on a Termite solid-state
thermostat (DNA-Technology, Russia) for 30 min. The
third set was sampled in quadruplicate and autoclaved at
110°C and 0.5 atm. for 30 min and an hour, respectively.
For the purity of the experiment, we used chicken
muscle tissue (breast fillet and drumstick), parenchymal
and hollow internal organs (kidney, heart, liver), skin
and cartilage, as well as minced pork meat containing
1% and 10% chicken.
Since chicken eggs are widely used in the food
industry, we had to determine their effect on the
PCR results. For this, we analyzed raw, boiled and
powdered eggs, as well as pancake flour. In addition, we
investigated 20% egg in minced pork, 10% raw egg in
water, and 10% egg in minced chicken. A model panel
was made from the above samples.
To eliminate the likelihood of PCR inhibition,
we used an internal control sample (ICS) which was
added to each test sample starting from the DNA
extraction stage.
DNA was extracted by the sorbent method
recommended by State Standard R 52723-2007X, using a
standard set of DNA-Sorb-S reagents (Central Research
Institute of Epidemiology, Russia). A number of
experiments performed with the extracted DNA showed
that a 100% chicken content (whether fillet, hollow
and parenchymal internal organs or connective tissue)
produced a threshold cycle (Ct) ≤ 15, whereas 10% and
1% chicken contents in minced meat produced Ct ≤ 18
and Ct ≤ 21, respectively. There is a correlation with the
ICS detection. When egg is present, the values decrease
to Ct ≥ 23 and the ICS also drops to Ct ≥ 28 due to
inhibition (Ct ≥ 24 with no inhibitors). DNA is obviously
less degraded in a pure product (raw and boiled egg)
than in egg powder, but Ct is inversely related: Ct ≥ 27
and Ct ≥ 20 for the egg powder sample and the ICS,
respectively; Ct ≥ 30 and Ct ≥ 28 for the raw and boiled
egg sample and the ICS, respectively.
Thus, we can conclude that raw and boiled eggs
contain PCR-inhibiting substances. The presence of
10% raw eggs in minced chicken leads to ICS Ct ≥ 27
versus ICS Ct ≤ 21 for 100% minced chicken. It is
impossible to evaluate the results when the reaction is so
strongly inhibited. Therefore, we chose a different DNA
extraction method described by Minaev et al. [2]. For
this, we used a SORB-GMO-B kit (Syntol, Russia) in
accordance with the manufacturer’s recommendations.
The PCR results are shown in Table 1. As we can see,
the ICS threshold cycle values indicate insignificant
inhibition of the reaction, confirming the right choice of
the DNA isolation method.
We selected those primers and probes that
fluoresce to the target DNA of chicken and the ICS
in the Green and Yellow channels. The solutions
X State Standard R 52723-2007. Foodstuffs and feeds. Rapid
method of identification of raw composition (molecular). Moscow:
Standartinform; 2007. 22 p.
of direct and reverse PCR primers and a probe at a
known molar concentration were diluted to a working
molar concentration of 6 μmol/dm3 a nd 3 μ mol/dm3,
respectively. For PCR, we used a dNTF solution (Syntol,
Russia), a PCR buffer-Flu and TaqF DNA polymerase
(Central Research Institute of Epidemiology, Russia).
The DNA extracted from each test sample was
analyzed in at least two replicates. For amplification
control reactions, we used recombinant plasmids based
on the pAL-2 vector (solutions of plasmid DNA at a
concentration of 0.01 mg/dm3) as positive reaction
controls. They were a plasmid containing a chicken
DNA fragment (pCh) and a plasmid of the internal
control sample (pICS).
For real-time PCR, we used Rotor-Gene Q amplifiers
(QIAGEN, Germany) and Rotor-Gene 6000 amplifiers
(Corbett Research Pty Ltd., Australia). We programmed
the device according to the operating instructions and
optimized the PCR-RT conditions for the duplex format.
The primer annealing temperature was 60°С, with a
PCR total temperature profile of 40 cycles.
RESULTS AND DISCUSSION
The PCR results for the model meat systems before
and after heat treatment (at various temperatures) are
presented in Table 1. The Background Threshold was
set at 15% and the Threshold was 0.05. We interpreted
the results based on the presence (or absence) of the
intersection between the fluorescence curve and a
threshold line set at an appropriate level. The conditions
for analysis were as follows: for a positive PCR control,
the threshold cycle values of Ct < 26 were present in the
Green and Yellow channels; for a negative extraction
control and a negative PCR control, the threshold cycle
values were absent in all the channels; the threshold
cycle value for the ICS was not lower than Ct ≤ 24 for
qualitative determination, since higher values indicate
PCR inhibition.
As we can see in Table 1, all the raw samples
containing meat or offal (including extremely low
concentrations) were identified at no later than the 19th
cycle; egg impurities, no earlier than the 25th cycle; and
egg powder and pancake flour, at the 29–30th cycle.
Interestingly, pure chicken meat, whether fillet or offal,
was identified at no later than the 14th cycle, while
connective tissue, no later than the 17th cycle. The
chicken contents of 10% and 1% produced Ct ≤ 15 and
Ct ≤ 19, respectively. These results allowed us to
conclude that:
– Ct < 15 indicated over 10% chicken in the test sample;
– Ct < 19 indicated over 1% chicken or high
concentrations of connective tissue in the test sample.
This conclusion makes it impossible to quantify the
chicken content at this stage of the study. However,
it leaves a possibility of a semi-quantitative analysis,
whose result can be expressed as “chicken content at
least N%”.
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The heat-treated samples containing meat or offal
(including extremely low concentrations, up to 1%)
were identified at no later than the 21st cycle and egg
impurities, no earlier than the 21st cycle. A 10% chicken
content in minced meat produced Ct ≤ 17, whereas
1% chicken showed Ct ≤ 21. From these results, we
concluded that Ct < 21 indicated more than 1% chicken
in the test sample.
The autoclaved samples containing chicken meat
or offal were identified at no later than the 17th cycle,
whereas the samples with extremely low concentrations
of chicken meat (up to 1%) and egg impurities, no later
than the 26th cycle. The chicken contents of 10% and
1% resulted in Ct ≤ 21 and Ct ≤ 25, respectively. Thus,
the detection of Ct < 25 indicated over 1% chicken
in the test sample.
Next, we proceeded to the development of a semiquantitative
method for determining chicken meat in
food products, since a quantitative method was not
possible due to the equality of cycles for the 10% minced
chicken samples and the connective tissue samples.
As adulterating a product with less than 1% meat (1 g
chicken meat per 1 kg of product) seems impractical,
we decided that the methodology should allow us
to determine the content of chicken in the product
in relation to several threshold values of calibration
samples, namely:
– “at least 1%” if Ct 10% < sample’s Ct ≤ Ct 1%;
– “at least 10%” if Ct 50% < sample’s Ct ≤ Ct 10%;
– “high content” if the sample’s Ct ≤ Ct 50%;
– “low DNA, possible egg presence” if the sample’s
Ct > Ct 1%.
Further, we evaluated the following criteria:
sensitivity and specificity of the primers, detection
limits, and a range of values for calibration samples
and internal control samples. Each experiment was
performed by two different researchers, at different
times, with reagents of different series, on different
amplifiers of the same type. Each sample was tested in
duplicate.
To assess the specificity of PCR, we created a panel
of DNA samples isolated from chicken, pork, beef,
Table 1 PCR results for model samples
Product Weight content, % Threshold cycle of the model sample (chicken meat content)
Not heat-treated 99°С, 30 min 110°С, 0.5 atm., 1 h
Minced breast 100 12.67 13.54 16.26
Minced drumstick 100 12.54 13.39 14.62
Minced liver 100 11.92 13.01 15.52
Minced kidneys 100 12.15 13.83 16.01
Minced heart 100 11.64 13.88 15.04
Cartilage 100 14.97 16.81 19.23
Skin 100 16.04 18.16 22.08
Minced chicken breast and pork 10 14.26 16.44 20.34
Minced chicken breast and pork 1 18.26 20.16 25.72
Liquid egg 100 24.29 26.89 29.05
Liquid egg 10 33.40 32.99 –
Minced pork and egg 20% egg 22.00 22.81 25.91
Minced chicken breast and egg 10% egg 13.29 14.91 16.91
Chicken ovalbumin (egg powder) 100 27.65 29.34 –
Pancake flour 4%* 28.95 30.06 –
* The average amount of egg powder in 12 formulations
Figure 1 Specificity assessment of the chicken identification methodology
(а) Chicken meat DNA, Yellow (b) ICS, Green
Norm. fluoresc.
Cycle
0.5
0.4
0.3
0.2
0.1
Threshold
5 10 15 20 25 30 35 40
Norm. fluoresc.
Cycle
0.30
0.25
0.20
0.15
0.10
0.05
0.00 5 10 15 20 25 30 35 40
Threshold
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Table 2 Specificity assessment of the duplex PCR system for chicken identification
Expected amplification result Actual amplification result, threshold cycle values, Сt ± SD Name
Replicate № 1 Replicate № 2 of sample
FAM, ICS Yellow, chicken FAM, ICS Yellow, chicken FAM, ICS Yellow, chicken
+ + 21.66 ± 0.05 12.76 ± 0.18 21.61 ± 0.10 13.46 ± 0.01 chicken
+ – + – + – pork
+ – + – + – beef
+ – + – + – goat
+ – + – + – mink
+ – + – + – turkey
+ – + – + – quail
+ – + – + – duck
+ – + – + – horse
+ – + – + – rabbit
+ – + – + – cat
+ – + – + – dog
+ – + – + – sheep
+ – + – + – Ci*
– – – – – – –C**
Table 3 Sensitivity of the duplex PCR system
(initial concentration of plasmid DNA – 4 ng/μL)
Number of genomic
copies in the reaction
Сt ± SD,
Yellow (chicken)
Сt ± SD,
Green (ICS)
20 000 23.16 ± 0.10 24.41 ± 0.15
2 000 26.87 ± 0.10 28.19 ± 0.05
200 30.58 ± 0.56 32.01 ± 0.18
20 34.00 ± 0.79 35.29 ± 1.07
2 – –
Table 4 PCR results for LOD determination
Chicken DNA
content, %
Threshold cycle
Ct ± SD for raw
and cooked products
Threshold
cycle
Ct ± SD
Chicken
(Yellow)
ICS
(Green)
Chicken
(Yellow)
ICS
(Green)
10 16.06 ± 0.11 23.15 ± 0.03 24.03 ± 0.12 23.15 ± 0.04
1.0 19.16 ± 0.03 23.18 ± 0.20 26.15 ± 0.02 23.18 ± 0.19
0.1 21.84 ± 0.28 23.02 ± 0.01 28.73 ± 0.29 23.02 ± 0.02
0.01 24.56 ± 0.01 23.25 ± 0.03 30.35 ± 0.02 23.25 ± 0.02
0.001 26.56 ± 0.23 22.29 ± 0.03 32.46 ± 0.19 22.29 ± 0.04
turkey, quail, duck, horse, mink, rabbit, cat, dog, goat,
and sheep. The results are shown in Fig. 1 and Table 2.
Within the proposed panel, the chicken DNA
identification methodology showed 100% specificity:
we observed the ICS amplification only on the Green
channel and the target chicken DNA on the Yellow
channel.
The assessment of the control panel for validation
confirmed a 100% convergence of the results.
To determine the analytical sensitivity of the
primers, we isolated DNA from a sample of 100%
chicken meat and prepared a series of 10-fold dilutions.
The maximum dilution was determined which allowed
reproducible (in duplicate) detection of DNA.
In addition, we used plasmid DNA solutions at a
specified concentration containing a cloned chicken
gene fragment and a ICS fragment. Two series of
ten-fold dilutions were prepared in a TE buffer with
various concentrations: series № 1 – pICS plasmid
DNA solution; series № 2 – pCh plasmid DNA solution.
The initial concentration of plasmid DNA in each
series was 4 ng/μL, which corresponds to ~ 20 000 genomic
copies in PCR (5 μL of a DNA solution for a
25 μL reaction). The results are presented in Table 3.
To determine the absolute limit of detection (LOD)
at which the PCR method is able to detect and quantify
chicken genetic material, we performed 10 PCRs,
with 5, 10, 20, and 40 genomic copies of chicken DNA
in each. Our PCR methodology detected chicken even
in the strongest dilution, with only five genomic copies
in the PCR.
To determine the limit of detection of chicken and
egg products in multicomponent raw and heat-treated
products, we used a number of model samples prepared
in two replicates and containing 10, 1.0, 0.1, 0.01, and
0.001% chicken in minced pork (isolated DNA). The
samples were preliminarily cooked at 99°С for 30 min.
To determine the LOD of chicken and egg products in
canned foods, the model samples were autoclaved at
110°C and 0.5 atm. The minimum chicken content in
minced pork was determined, at which chicken DNA
was reproducibly (in duplicate) detected. The results are
shown in Table 4.
* Ci – isolation control (shows the absence of inhibition at the stage of DNA isolation)
** –C – negative PCR control (shows the purity of the reaction, mixes, and the laminar, as well as the absence of contamination)
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Table 5 Constancy of Ct ranges for calibration samples
Series Calibration sample’s Ct
Cooked for 30 min at 99°C Autoclaved for 60 min at 0.5 atm
1% 10% 50% 1% 10% 50%
1 18.97 18.68 16.08 16.21 13.85 13.91 27.92 27.89 24.05 23.99 19.74 19.85
2 19.63 19.70 15.84 16.02 11.78 12.13 25.34 25.40 24.63 24.52 20.04 20.21
3 19.34 19.55 17.04 17.15 13.81 13.89 27.83 27.95 22.16 22.03 19.10 19.03
4 19.22 19.43 17.54 17.66 13.77 13.98 26.45 26.53 22.25 22.15 20.93 20.84
5 19.93 20.15 14.50 14.37 12.88 13.00 27.27 27.17 23.69 23.45 21.52 21.68
6 18.29 18.52 16.32 16.45 12.70 12.96 25.77 25.89 24.67 24.77 19.66 19.76
7 18.04 18.19 15.52 15.63 13.84 13.79 25.05 25.30 22.16 22.26 19.76 19.82
8 19.71 19.59 16.96 16.67 13.55 13.75 25.58 25.41 22.25 22.05 19.34 19.11
9 19.94 20.17 14.43 14.87 12.67 12.56 26.90 26.99 23.70 23.84 20.81 20.94
10 20.40 20.62 14.03 14.25 13.02 13.17 27.60 27.52 24.71 24.66 21.70 21.64
11 18.01 18.14 16.47 16.44 11.99 12.21 25.53 25.64 22.86 23.02 21.45 21.15
12 20.78 20.84 15.33 15.66 12.40 12.23 26.15 26.45 24.35 24.23 20.93 21.03
13 20.96 20.86 17.83 17.64 12.56 12.71 25.74 26.03 22.88 23.09 21.63 21.72
14 20.85 20.91 17.98 17.83 12.83 13.01 27.14 27.33 24.64 24.98 21.12 21.23
15 20.21 20.16 15.25 15.44 13.34 13.43 26.59 26.84 24.10 24.28 21.84 21.92
Maximum, Ct 20.96 20.91 17.98 17.83 13.85 13.98 27.92 27.89 24.71 24.98 21.84 21.92
Minimum, Ct 18.01 18.14 14.03 14.25 11.78 12.13 25.05 25.30 22.16 22.03 19.10 19.03
SD 0.94 0.93 1.20 1.10 0.65 0.63 0.92 0.88 0.98 1.02 0.91 0.93
RSD 4.81 4.70 7.49 6.82 5.02 4.84 3.47 3.33 4.16 4.34 4.40 4.50
* SD – standard deviation, RSD – relative standard deviation
The limit of detection for chicken DNA ranged from
0.1 to 0.001% of the chicken content in the sample.
The methodology should allow us to assess the
content of chicken and egg products in food products
relative to several selected threshold values of
calibration samples. To prepare calibration samples of
various compositions for the semi-quantitative analysis
of raw and cooked products, we mixed 100% minced
chicken meat with 100% minced pork (1%, 10%, and
50% chicken) and heated at 99°C for 30 min.
We decided to evaluate both cooked and raw
products in relation to the values of heat-treated
calibrators, since fresh chicken meat was used to
prepare model samples of raw products, which cannot
be guaranteed by product manufacturers. Moreover,
samples for analysis do not always get delivered to the
laboratory directly, bypassing the stages of storage
or freezing, which increases the likelihood of DNA
degradation. The calibration samples for canned
products were autoclaved at 110°C and 0.5 atm. The
uniformity coefficient of the calibrators was 0.99 (99%).
To confirm the constancy of the calibrators’ Ct
ranges, we performed a series of tests. In particular,
we studied 15 series of calibration samples prepared
on different days, by different people, each in two
replicates. For each series, we determined the minimum
and maximum values of the threshold cycle on the
Yellow-chicken channel, a standard deviation, and a
relative standard deviation. The results are presented
in Table 5.
As a result, we selected the following threshold cycle
values on the “Yellow-Chicken DNA” channel for the
calibrators of:
– raw products and those subjected to light heat
treatment: 18 ≤ Ct 1% < 21; 14 ≤ Ct 10% < 18;
Ct 50% < 14;
– autoclaved products (canned food): 25 ≤ Ct 1% < 28;
22 ≤ Ct 10% < 25; Ct 50% < 22.
Also, a threshold cycle value of at least Ct ≤ 24 was
chosen as acceptable on the “Green-ICS” channel for
the calibrators (Ctics 1%, Ctics 10%, Ctics 50%) and the
negative control sample.
CONCLUSION
We developed a method (certified methodology)
for a semi-quantitative assessment of chicken content
in multicomponent food systems of varying degrees
of heat and mechanical treatment: raw, heat-treated,
canned, finely ground, and homogenized. Having tested
various DNA extraction methods, we concluded that
the guanidine-chloroform method reduces the content
of PCR-inhibiting substances compared to the sorption
method.
Our methodology was tested on model samples,
as well as product samples from retail stores, to
exclude the possibility of PCR inhibition by food
additives, stabilizers, emulsifiers, etc. With PCR,
we can distinguish between chicken meat and egg
products in raw and cooked products (over 21 cycles),
as well as canned foods (over 28 cycles). Our results
suggest that this methodology is suitable for analyzing
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multicomponent food products, raw materials, feeds, and
feed additives. In addition, it can identify the content
of chicken meat at a concentration of up to 1%, as well
as detect egg impurities and contamination of various
origins.
Taking into account the current need for
distinguishing adulteration from the inevitable
contamination on the production line, as well as
preventing adulteration of expensive raw materials
with chicken meat by introducing egg products, we
believe that our methodology could make a significant
contribution to the production of high-quality foods.
CONTRIBUTION
Each of the authors was directly involved in the
development, testing, and validation of the above
methodology, as well as in writing this article.
CONFLICT OF INTEREST
The authors state that there is no conflict of interest.

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