A versatile real-time PCR method to quantify bovine contamination in buffalo
products
M.G. Drummond
a,b,*,1, B.S.A.F. Brasil
a,b,1, L.S. Dalsecco
a, R.S.A.F. Brasil
c, L.V. Teixeira
d, D.A.A. Oliveira
a aLaboratório de Genética Animal, Departamento de Zootecnia, Escola de Veterinária, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, BrazilbValid Biotechnology research team, INOVA, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil cEscola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
dDepartamento de Tecnologia e Inspeção de Produtos de Origem Animal, Escola de Veterinária, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
a r t i c l e i n f o
Article history:Received 8 March 2012 Received in revised form 15 May 2012 Accepted 22 May 2012 Keywords: Food authentication Bovine Buffalo Real-time PCR Quantification
a b s t r a c t
Fraudulent species substitution in food products is a reality in many markets around the world. The consequences are serious and impact governments, consumers and producers. Molecular methods, including PCR-based techniques, are available to prevent species substitutions and to authenticate food content. Here, we describe a method for calculating the bovine and buffalo content in milk- and meat-derived food products. The method applies real-time PCR using primers designed to specifically amplify bovine or buffalo DNA. The amplification efficiencies of both primer sets were assessed using the TaqMan and SYBR Green systems. Both sets of primers showed satisfactory results. A quantification procedure is proposed and involves amplifying a sample with both primer sets and then normalizing the total DNA using the total non-normalized bovine and buffalo DNA. To correct for the potential deviations between the real and measured DNA quantity caused by biological differences between species, we propose the use of cali-bration curves generated from each analyzed matrix. These curves include a set of controlled admixtures of bovine and buffalo material. Using this method for dairy samples containing known bovine and buffalo content, the use of the calibration curve always approximated the measured to the expected quantities. This technique was then tested on commercial samples. Our method was efficacious and reliable and can be applied to the routine analysis of these products. Although we only tested the methodology on dairy products, it can also be applied to other food matrices, such as meat-derived products. A patent document for the technique has been submitted to the Brazilian Patent Office (INPIeInstituto Nacional da Propriedade Industrialewww.inpi.gov.br) under the number 014110002844.
Ó2012 Elsevier Ltd.
1. Introduction
The adulteration of foods with material from other species with greater availability and/or lower cost is observed worldwide. An example of this process is the addition of bovine milk to sheep, goat and water buffalo dairy products (Herman, 2001). Other animal products, such as seafood and meat-derived products, are also subjected to content substitution (Carvalho, Pimenta-Neto, Brasil, & Oliveira, 2011; González-Córdova, Calderón de la Barca, Cota, & Vallejo-Córdoba, 1998; Lahiff et al., 2001; Soares, Amaral,
Mafra, & Oliveira, 2010). These adulterations may pose health risks to individuals with metabolic disorders or particular allergies, such as to bovine-derived milk (Woolfe & Primrose, 2004). The food industry may also suffer from financial losses as a result of the ethical, religious and cultural objections of consumers (Woolfe & Primrose, 2004). The negative impacts of food adulteration compel governing bodies to create more efficient public policies that combat food mislabeling and fraud (Herman, 2001). Analytical tools are therefore necessary, particularly if the food changes drastically after processing, thus hampering or precluding the visual identification of the product (Woolfe & Primrose, 2004).
In recent years, a variety of methods have been developed to detect the fraudulent admixture of milk or meat from different species. These techniques include lipid (Saeed, Ali, Rahman, & Sawaya, 1989), protein (Addeo, Pizzano, Nicolai, Caira, & Chianese, 2009; Cozzolino, Passalacqua, Salemi, & Garozzo, 2002; Cuollo et al., 2010; González-Córdova et al., 1998; Guarino et al., 2010) and DNA assays (Lanzilao, Burgalassi, Fancelli, Settimelli, & Fani,
*Corresponding author. Laboratório de Genética Animal, Escola de Veterinária, Universidade Federal de Minas Gerais (UFMG), Avenida Antônio Carlos 6627, 31270-010 Belo Horizonte, Minas Gerais, Brazil. Tel.:þ55 31 34092206; fax:þ55 31 34913963.
E-mail addresses:marcela.drummond@validbiotech.com,marceladrummond@ gmail.com(M.G. Drummond).
1 These authors contributed equally to this work.
Contents lists available atSciVerse ScienceDirect
Food Control
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / f o o d c o n t
0956-7135Ó2012 Elsevier Ltd. doi:10.1016/j.foodcont.2012.05.051
Open access under the Elsevier OA license.
2005;Maccabiani et al., 2005;Reale, Campanella, Merigioli, & Pilla, 2008;Zhang, Fowler, Scott, Lawson, & Slater, 2007). The lipid and protein analyses, however, are laborious and most of the time cannot be applied to heated or chemically treated products, such as cooked or fermented foods (López-Calleja et al., 2007; Mayer, 2005). In addition, they cannot distinguish between phylogeneti-cally related species, such as cattle and buffalo (Bellis, 2003; Branciari, Nijman, Plas, Di Antonio, & Lenstra, 2000;Zhang et al., 2007). In contrast, the DNA-based methods are more practical, sensitive and robust (Cuollo et al., 2010;Guarino et al., 2010).
Among the DNA-based techniques, PCR is often the method of choice for identifying the species present in food products. Minimal DNA quantities can be detected using species-specific primers (Mafra, Ferreira, & Oliveira, 2007). The use offluorescent probes or chemicals during a PCR reaction (real-time PCR) permits DNA detection during the amplification cycles. This technique can be used to quantify the initial DNA in a given sample (Kubista et al., 2006). Real-time PCR is highly sensitive and is useful for situa-tions in which occasional contamination is acceptable ( López-Calleja et al., 2007;Sawyer, Wood, Shanahan, Gout, & McDowell, 2003).
In the present study, we propose a real-time PCR system for quantifying the bovine and buffalo material present in animal-derived products. The TaqMan system was used, even though we demonstrate that the technique described can also be used with the SYBR Green system. Although previous studies have shown the utility of real-time PCR for quantifying the contaminants in products derived from diverse species (López-Andreo, Lugo, Garrido-Pertierra, Prieto, & Puyet, 2005;López-Calleja et al., 2007;Sawyer et al., 2003; Zhang et al., 2007), only one study specifically addressed the adul-teration of buffalo products (Lopparelli, Cardazzo, Balzan, Giaccone, & Novelli, 2007). Nonetheless, the reported techniques exhibit high measure variability mainly due to species-specific DNA content variations in the diverse food matrices. In contrast, our method is calibrated using a wide range curve of controlled frauds to normalize the DNA quantity according to the food matrix. This process, together with the proposed calculation method, makes the present methodology more accurate and versatile than the previously described techniques.
2. Methodology
2.1. Sample collection and DNA extraction
Pure dairy and meat samples derived from bovine (Bos taurus taurusorBos taurus indicus) and buffalo (Bubalus bubalis) material were used to test the efficiency of the primer/probe sets (described in section2.4). These samples were also applied to create a cali-bration curve (section2.6) and to test the technique presented here (section 2.7). Commercial samples were acquired from local markets to test the usefulness of the method.
DNA was extracted from dairy products using methods described byBoom et al. (1990), with some modifications. The cheese samples were sliced, and 6 g (total weight) was incubated overnight with 5 mL of extraction buffer (5 M guanidinium thiocyanate, 50 mM TriseHCl pH 8.0, 20 mM EDTA, 1.3% Triton X-100). The samples were then centrifuged (5 min, 2500g), and the supernatant was diluted with 5 mL of extraction buffer. One hundred andfifty microliters of activated silica (prepared according toBoom et al., 1990) was added to each sample. The mixture was vortexed and incubated under constant agitation at room temperature for 10 min. The silica, now bound to DNA molecules, was then rapidly centrifuged. It was washed three times with 500
m
L of washing buffer (5 M guanidinium thiocyanate, 50 mM TriseHCl pH 8.0), twice with 500m
L of 70% ethanol and once with 500m
L of acetone. The silica particles were dried by incubation at 95C for 5 min. Finally, the DNA was recov-ered by adding 150m
L Milli-Q water, vortexing and incubating for 10 min at 95C. The silica was spun down and discarded, and the DNA-containing supernatant was recovered.The DNA was extracted from meat products (5 mg) using a phenolechloroform protocol, as described bySambrook, Fritsch, and Maniatis (1989).
The DNA quantity and quality were assessed using a NanoVue Spectrophotometer (GE Healthcare, Waukesha, WI, USA). The 260/ 280 nm measurement was considered a marker of DNA quality. Negative controls (containing no dairy or meat sample) were included in each extraction procedure.
2.2. Primer and probe design
Bovine (Bos)-specific primers (Table 1) were designed to amplify the mitochondrial gene CytB. They were modified from that described byZhang et al. (2007)for use with MGB TaqMan probes (Applied Biosystems, Carlsbad, CA, USA). Buffalo (Bub)-specific primers (Table 1) were designed to amplify the mitochondrial gene 16S. The MGB TaqMan probes were designed based on the conservedCytB and 16Sgene regions of Bos taurus,Bos indicus, Bubalus bubalis,Capra hircus,Ovis ariesandSus scrofa. Sequences of theCytB and 16S genes were obtained from the NCBI GenBank database (Benson, Karsch-Mizrachi, Lipman, Ostell, & Sayers, 2010) and aligned using the ClustalW algorithm (Chenna et al., 2003) in MEGA version 5 (Tamura et al., 2011). The Primer Express Software (Applied Biosystems) was used to design primers and probes. 2.3. Real-time PCR reactions
All real-time PCR reactions were conducted in triplicates using the 7500 Real-Time PCR System (Applied Biosystems). Each reac-tion contained 12.5
m
L of TaqManÒ Universal PCR Master Mix (Applied Biosystems), 400 nM of each specific primer, 200 nM of specific probe and 2m
L of target DNA. The DNA concentrations used in each reaction are described in theResults section. Five micro-grams of BSA (Bovine Serum Albumin) were added to the reactionsTable 1
Primer/probe sets used in this study.
Primer/probe Typea Sequence (50/30) Target
BosF Forward primer CGGAGTAATCCTTCTGCTCACAGT CytB (bovine)
BosR Reverse primer GGATTGCTGATAAGAGGTTGGTG CytB (bovine)
MamCytB TaqManeMGB probe FAMeCATGAGGACAAATATCeMGBb CytB (mammals)
Bub2F Forward primer TCAGCCCAAAGAAAAATAAACCA 16S (buffalo)
Bub2R Reverse primer GTCACCCCAACCGAAACTGT 16S (buffalo)
Bub16S TaqManeMGB probe FAMeTAAGGARTAACAACAMTCTeMGBc 16S (mammals)
aOnly the primer sets were used with the SYBR Green system.
bIn the test, the FAMfluorophore was used to target the probes. Otherfluorophores can be used in its place. c The Bub16S probe was designed to undergo degeneration. According to IUPAC, R¼A or G, M¼A or C.
containing meat-derived DNA. The reaction conditions were applied as follows: 50C for 2 min, 95C for 10 min and 40 cycles of 95C for 15 s, 58C for 15 s and 60C for 1 min. In the SYBR Green reactions, 12.5
m
L of SYBRÒGreen PCR Master Mix (Applied Bio-systems) were used instead of the TaqMan mix, and no probe was added. A dissociation curve was added at the end of PCR cycling, as follows: 95C for 15 s, 60C for 1 min, 95C for 30 s and 60C for 15 s. The dissociation curves were analyzed after each experiment to avoid non-specific amplification.The reactions were analyzed using the 7500 Software (Applied Biosystems). The program automatically corrected the basalfl uo-rescence measurement. The optimal thresholds werefixed for each primer/probe set (0.127651 for Bos and 0.025158 for Bub). 2.4. Amplification efficiency
Dilution curves using pure bovine or buffalo DNA were used to verify the amplification efficiency of each primer/probe set (Taq-Man) or primer (SYBR Green). The template DNA was diluted in a 1/ 2 series and amplified as specified above. Using the 7500 Software, Cycle threshold (Ct)DNA quantity curves were constructed. The curve slope, amplification efficiency (E¼[101/slope]1) andR2 were also calculated using the program. TheR2values were used to verify thefit of the trend line to the points on the curve. The Ct range was defined as the interval between the Ctminand Ctmaxof
the curve. Similarly, the initial DNA quantity range was defined as the lower and upper limits of total DNA on the curve. The specificity (given in Ct) of each primer/probe set was measured using non-targeted DNA at the highest quantity used in the target DNA curve. Similarly, the primer/probe sets were also tested against non-target DNA derived from goats (C. hircus) and sheep (O. aries). 2.5. Data analysis
The quantification protocol used in this study included the real-time amplification of each sample using the bovine and buffalo primer/probe sets. Thus, it was possible to determine the quantity of cattle or buffalo DNA relative to the total material derived from both species. The quantity of material derived from each species was calculated using the following equations:
Relative quantity of Bos material ¼ 2CtBos=2CtBosþ2CtBub (1)
or
Relative quantity of Bub material ¼ 2CtBub=2CtBosþ2CtBub (2)
where CtBos and CtBub indicate the reaction cycle in which the sample reached the pre-defined cycle threshold for the Bos or Bub primer/probe set, respectively.
Equations(1) and (2)are based on the non-normalized quan-tities of Bos and Bub material in a given sample. According to Schmittgen and Livak (2008), the non-normalized quantity N of a specific DNA target in a sample is given by 2Ctfor an approxi-mately 100% efficient amplification reaction. Thus,
NBos ¼ 2CtBos (3)
and
NBub ¼ 2CtBub: (4)
By normalizing using the sum of the non-normalized quantities of Bos and Bub material, we can deduce the Equations(1) and (2).
2.6. Calibration curves and automated quantification
Calibration curves were constructed for the dairy samples using points of controlled ratios. We used this procedure to correct the measured to the real quantity of material from bovine and buffalo species. Pure cheese samples were weighed (final weight: 6 g) and mixed in the following proportions of Bos/BosþBub content: 100%, 99%, 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 1%, and 0%. Each ratio was tested through the DNA extraction and amplification procedures (TaqMan system) specified in sections2.1 and 2.3.
Two independent curves were built using the same points of controlled ratios. The mean relative bovine quantity measured for each ratio was plotted against its real bovine quantity. Using Microsoft Excel 2010Ò, a trend line was added to the curve, and the
first degree equation describing the curve was recorded. The equation obtained from the calibration curve was then used to adjust the measurements from the assays of other buffalo/bovine dairy samples. A software program (named Calibration Software) was designed for Microsoft Excel 2010Ò using Visual Basic for Applications. This program automates the quantification calcula-tions. The “Results” file exported from the 7500 Software con-taining Cts data must be used as the inputfile.
2.7. Method application
To test the developed method, controlled ratios derived from pure cow and buffalo cheeses were assembled as described in section 2.6. They consisted of Bos/Bos þ Bub content in the following ratios: 7, 16, 30, 66, 78, 87, 97%. Then, 10 commercial samples were analyzed. The 0, 100 and 50% Bos/BosþBub samples were processed as controls with each experiment. The DNA was extracted, and the PCR reactions were performed (TaqMan system) as previously described. The Resultfiles from the 7500 Software were used with our Calibration Software to calculate the real bovine content of the tested samples.
3. Results
3.1. Validation of amplification efficiency
The amplification efficiencies of the Bos and Bub primer/probe set (TaqMan) and primers (SYBR Green) were analyzed by testing the DNA extracted from pure meat and cheese samples from both species. The amplification efficiencies, slopes,R2, specificity limits and ranges of Ct and template DNA quantities obtained for each set are summarized in Table 2. All combinations of primers, PCR systems and food matrices yielded efficiency values between 90 and 100% (Table 2).R2values were 0.97 at minimum (Table 2). The melting curves of the Bos and Bub primers yielded only one peak with the expected TM values (Bos ¼77 C; Bub ¼75 C). Both primer/probe sets showed a Ct31 when tested against non-target DNA from the other species (Table 2). DNA derived from pure sheep and goat dairy samples were also tested. The same level of
speci-ficity was found (Ct31edata not shown). Thus, Cts above the threshold of cross-species DNA amplification (31) were considered to be negative.
3.2. Specific DNA quantification
To test the accuracy of the quantification method, DNA samples obtained from two independent sets of controlled bovine/buffalo cheese mixtures (calibration curves) were amplified using the TaqMan system. Dairy samples were chosen only to demonstrate the utility of our method. Any other food matrix, such as hamburgers and sausages, could also have been used. Equation(1),
described in the materials and methods section, was used to calculate the bovine DNA in the mixtures. To maintain the Cts within the efficiency range of both primer sets, 220 ng/sample (2
m
L of a 110 ng/m
L sample DNA, as measured by spectrophotometric analysis) were used in all experiments. Good correlations between the measured and expected values were observed for both sets of samples (Fig. 1). Only minor deviations were observed. Interest-ingly, with the exception of the 10%, 20% and 40% Bos/BosþBub ratios, the results of the two independent test sets showed similar patterns. The plotted points for the respective ratios in the two sets were either both above or both below the expected values.3.3. Specific DNA calibration curves
The deviations from the expected values described above (Fig. 1) seemed to follow reproducible patterns. Thus, they may have been a result of intrinsic systemic characteristics or variations in the analyzed species and/or food matrices. In contrast, variations due to experimental manipulation or technical errors would have been expected to vary randomly. The observed phenomenon was predictable because the number of somatic cells in a sample and the number of mitochondria per cell may vary among the milk of different species (Lopparelli et al., 2007; Zhang et al., 2007). To correct for these intrinsic variations, we averaged the measured relative quantity of Bos material for each ratio in the two test sets to build a Bos/Bub reference curve for the dairy samples. These values were plotted, and a trend line was added using Microsoft Excel
2010Ò (Fig. 2). The measured R2 value (0.998) showed that the
points were well represented by the straight trend line. The derived trend line equation was then applied to the following experiments using an automated spreadsheet (Calibration Software). This step allowed us to correct the measured to the real quantity of the bovine and buffalo material present in a sample.
3.4. Method validation
Admixtures of pure bovine and buffalo dairy samples were again tested in controlled ratios. DNA was extracted and amplified from these samples using the methodology described above for TaqMan system. The Calibration Software was then used to calculate the real bovine content in each sample. As shown inTable 3, the quantifi -cation method was effective in determining the real bovine content of a given sample. The accuracy of the method was very high. The measured value was at most 12.5% greater or less than the expected value (e.g., expected 0.16 / measured 0.14). Importantly, the calibration curve successfully corrected the calculated quantifi ca-tion to the expected value in all cases (Table 3).
Commercial samples purchased from local retailers were also analyzed using our method (Table 4) to show its applicability to the routine analysis of commercial buffalo products. Controls with 50, 100 and 0% Bos/BosþBub content were tested with each experi-mental sample. A maximum of 6% deviation from the expected value was observed for these controls (e.g., expected 0.5/measured 0.47).
Fig. 1.Expected and measured bovine content in the diverse ratios of controlled mixtures of two calibration curves. Known amounts of bovine and buffalo cheese pure samples were weighed and mixed to create ratios of controlled contamination for two calibration curves. DNA from the resulting admixtures was extracted and amplified using the Bos and Bub primer/probe sets and the TaqMan system. Equation(1)was used to calculate the proportion of the bovine content in each sample.
Table 2
Data from efficiency experiments using Bos and Bub amplification sets.a PCR system Primer/probe
set
Sample material
Efficiency (%) Slope R2 Ct range Initial DNA amount range
Number of dilution points in the curve
Specificity limit (Ct)
TaqMan Bos Meat 96.0 3.42 0.99 25.2e30.5 0.098e0.003 ng 6 37
Bos Cheese 91.6 3.54 0.99 16.8e24.0 10e0.08mg 8 31
Bub Meat 90.1 3.58 0.99 28.7e32.9 0.08e0.005 ng 5 35
Bub Cheese 92.7 3.51 0.97 17.8e25.2 10e0.08mg 8 35
SYBR Greenb Bos Cheese 93.1 3.49 0.99 16.7e22.0 1.25e0.04mg 6 40
Bub Cheese 92.9 3.50 0.98 15.7e20.0 1.25e0.08mg 5 40
aEach variable was measured as described in materials and methods section2.3.
4. Discussion
Several methods are currently being applied to authenticate food products, as reviewed byReid, O’Donnell, and Downey (2006). However, the complexity of food matrices and small quantities of the contaminant present challenges to the sensitivity of the avail-able techniques (Vallejo-Cordoba & González-Córdova, 2010). Among the DNA-based technologies, PCR was chosen for the present study because it can quickly and specifically amplify small DNA quantities (Mafra et al., 2007)eeven after heavy food pro-cessing (Rodríguez-Ramírez, González-Córdova, & Vallejo-Cordoba, 2011). Mitochondrial DNA was chosen as the amplification target because of its relative cellular abundance. This attribute increases assay sensitivity (Lopparelli et al., 2007). Furthermore, the genetic regions that were targeted are relatively small (approximately 100 bp). Thus, even DNA molecules degraded by the thermal, chemical and/or physical processes through which food is prepared can be detected.
Real-time PCR is commonly used to measure the quantity of a DNA template in a given sample. Several methods are used to calculate DNA quantity based on real-time PCR Ct results. One such method is the relative quantification using comparative Ct (Livak & Schmittgen, 2001). For this method, an internal control must be applied to normalize the DNA quantities in different samples (Livak & Schmittgen, 2001). In the majority of the studies that use real-time PCR to quantify contaminant species, normalization is
performed using a eukaryotic sequence common to all involved species (López-Andreo et al., 2005; López-Calleja et al., 2007; Lopparelli et al., 2007;Sawyer et al., 2003). However, the amplifi -cation efficiency of universal primers can be affected by the pres-ence of more than one type of DNA in the sample and can vary depending on the species involved (López-Andreo et al., 2005). This deviation in amplification efficiency may be a result of minor sequence variations between or within species that are out of the researcher’s control. Taris, Lang, and Camara (2008)showed that a large number of polymorphisms can interfere with real-time PCR. In fact, when we used universal primers to normalize the DNA quantities of diverse samples, we found a significant difference in the amplification efficiencies obtained with pure or mixed samples (data not shown). Thus, we used the sum of the non-normalized Bos- and Bub-specific measures to normalize DNA quantity. Our method is corroborated by the propositions that the non-normalized quantity of a given DNA molecule can be calculated using 2Ct(Schmittgen & Livak, 2008).
To validate our technique, we analyzed the amplification effi -ciencies of the primer/probe sets. This standard validation step is crucial for real-time PCR experiments in which more than one target is amplified. In such cases, it is essential that both primer/ probe sets exhibit amplification efficiencies of 100% (10%). This condition was true for the two sets used in the present study. The test developed here can be conducted using the Bos and Bub sets with the TaqMan or SYBR Green systems for dairy products (as shown inTable 3). The amplification efficiencies were also tested using the TaqMan system on meat-derived products, and the results were satisfactory.
The quantification method proposed here was efficient in calculating the relative bovine and buffalo quantities. We showed high correlations between the measured and expected values in the calibration curves (Fig. 1). The minor deviations may have been a result of differences in the number of mitochondria per cell in the different species. They may also have been attributable to the variations of somatic cell numbers (and therefore DNA quan-tity) in the milk of the different species (Lopparelli et al., 2007; Zhang et al., 2007). To minimize these deviations, we applied calibration curves to adjust the measured to the real relative content of bovine or buffalo material in a sample. Interestingly, when testing random ratios of controlled contamination, we observed that the calibration curve always corrected the calcu-lated quantity to the expected quantity (Table 3). Although the measurement deviations of the bovine/buffalo mixtures appear to be minor, the variability intrinsic to other mixtures may be significant. One example of this latter case may be mixtures of sheep and bovine dairy products (Drummond, M. G. and Brasil, B. S. A. F. et al.,eunpublished results).
Table 3
Proportion of expected and measured Bos content in controlled contamination experiments.
Ratios Expecteda Measuredb After correctionc
0% 0.00 0.00 0.00 7% 0.07 0.07 0.07 16% 0.16 0.13 0.14 30% 0.30 0.30 0.30 50% 0.50 0.46 0.47 66% 0.66 0.65 0.66 78% 0.78 0.75 0.76 87% 0.87 0.85 0.86 97% 0.97 0.96 0.97 100% 1.00 1.00 1.00
aExpected Bos content based on the bovine material added to the mixture. b Proportion of Bos content calculated using the method described in this manuscript and Equation(1).
c Corrected proportion of Bos content adjusted using the calibration curve.
Table 4
Proportion of Bos content in commercial samples.
Sample Measureda After correctionb
1 0.00 0.00 2 0.00 0.01 3 0.05 0.06 4 0.25 0.25 5 0.39 0.40 6 0.04 0.05 7 1.00 1.00 8 0.00 0.00 9 0.00 0.00 10 0.00 0.00
aProportion of Bos content as calculated using the method described in this manuscript and Equation(1).
bProportion of Bos content corrected using the calibration curve.
Fig. 2.Calibration curve used to correct the error in the measured bovine quantity. The mean bovine quantity measured for the different ratios in the two controlled contamination experiments was plotted against the expected Bos content. A trend line was added using Microsoft Excel 2010Ò. The trend line equation was applied in the following experiments to adjust for the real bovine content of buffalo-derived dairy samples.R2was calculated to assess thefit of the trend line to the measured points.
Other authors have already applied similar approaches for quantifying the contamination present in complex mixtures ( López-Andreo et al., 2005;López-Calleja et al., 2007;Lopparelli et al., 2007; Sawyer et al., 2003). The method described bySawyer et al. (2003) andLopparelli et al. (2007)requires a curve to be constructed each time an experiment is performed. Furthermore, the authors report that their method is inefficient for dealing with between-species variations that can lead to inaccurate quantification.López-Andreo et al. (2005)used a calibration curve similar to the described here to test their quantification method in products derived from pork, chicken, turkey, cattle and lamb. The results showed greater error than those in our study, especially when the contamination level was below 5%. López-Calleja and colleagues (2007)used a calibration curve to quantify the amount of cow’s milk in ewe’s milk. However, their method only covers 0.5e10 % of contamination levels.
Dairy samples were chosen to show an application of our method because the difference between the expected and observed quantities may be large in these samples. This assumption is based on the knowledge of the somatic cell number variation in the milk of different species (Boutinaud & Ammes, 2002). Furthermore, cheeses are complex matrices and are processed by pasteurization processes that may damage DNA to a greater extent than processes applied to produce sausages and hamburgers, for example. Inter-estingly, the DNA damage in cheese samples may explain the relatively large template DNA quantities required to achieve high-quality efficiency curves in comparison with the meat-derived curves (Table 2).
Overall, the technique described here improves upon the currently available methods for quantifying the bovine and buffalo material present in processed food products. Our method is appli-cable to forensic investigations of food mislabeling and can aid regulatory agencies in controlling food fraud.
Disclosure statement
The method described here is the subject of the patent docu-ment 014110002844 at Brazilian Patent Office (INPIewww.inpi. gov.br) and is protected under Brazilian regulations. MGD, BSAFB, RSAFB and DAAO are the inventors on this patent document.
Funding source
This work was supported by the Brazilian agencies CNPq (INCT 573899/2008-8) and FAPEMIG (INCT APQ-0084/08). The funders had no role in study design, data collection, analysis and inter-pretation, writing of the report or the decision to publish.
Acknowledgments
The authors thank Dr. Eduardo Bastianetto for his valuable contributions to this work.
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