High resolution melting (HRM) analysis of DNA – Its role and potential in food analysis

Food Chemistry 158 (2014) 245–254

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Review

High resolution melting (HRM) analysis of DNA – Its role and potential in food analysis Barbara Druml, Margit Cichna-Markl ⇑ Department of Analytical Chemistry, University of Vienna, Währinger Straße 38, A-1090 Vienna, Austria

a r t i c l e

i n f o

Article history: Received 12 July 2013 Received in revised form 2 February 2014 Accepted 19 February 2014 Available online 28 February 2014 Keywords: High resolution melting (HRM) Food Adulteration Species identification Pathogenic microorganisms Genetically modified organisms (GMO)

a b s t r a c t DNA based methods play an increasing role in food safety control and food adulteration detection. Recent papers show that high resolution melting (HRM) analysis is an interesting approach. It involves amplification of the target of interest in the presence of a saturation dye by the polymerase chain reaction (PCR) and subsequent melting of the amplicons by gradually increasing the temperature. Since the melting profile depends on the GC content, length, sequence and strand complementarity of the product, HRM analysis is highly suitable for the detection of single-base variants and small insertions or deletions. The review gives an introduction into HRM analysis, covers important aspects in the development of an HRM analysis method and describes how HRM data are analysed and interpreted. Then we discuss the potential of HRM analysis based methods in food analysis, i.e. for the identification of closely related species and cultivars and the identification of pathogenic microorganisms. Ó 2014 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . High resolution melting (HRM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Important aspects in the development of an HRM analysis method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Current role of HRM analysis in clinical research and diagnostics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development and application of HRM analysis methods for food analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Identification and differentiation of varieties and closely related species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Genotyping and serotyping of pathogenic microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Screening for genetically modified organisms (GMO). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Detection of food allergens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Food safety has always been an important issue. The problem situation has, however, completely changed. Recent advances in molecular biology and molecular genetics make it possible to detect and characterise pathogenic microorganisms and to develop strategies to eliminate or circumvent their health effects. Contamination with pathogenic microorganisms is, however, still a worldwide health concern, causing millions of cases of foodborne diseases each ⇑ Corresponding author. Tel.: +43 1 4277 52374; fax: +43 1 4277 9523. E-mail address: [email protected] (M. Cichna-Markl). http://dx.doi.org/10.1016/j.foodchem.2014.02.111 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

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year. In contrast to spoiled foodstuffs which pose a risk for all individuals allergenic foods are a health problem for only about 3% of the adult population. Food allergic patients must avoid the consumption of foods they are allergic to in order to prevent the occurrence of allergic reactions. Although so far studies have failed to demonstrate that genetically modified (GM) foods cause negative effects on human health the benefits and risks of GM foods have been discussed controversially. In addition to food safety, food quality has gained increasing attention. Although commercial food products not only have to be safe but also authentic, adulterated foodstuffs have been detected on the market. Specific and sensitive analytical methods are necessary in order to be able to verify if commercial foodstuffs comply with national

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and international legal regulations. Due to the stability of DNA, DNA based methods play an increasing role in food safety control and food adulteration detection. Recent papers show that high resolution melting (HRM) analysis, an advanced method based on the melting behaviour of double stranded DNA, is a novel and interesting approach. In the following we introduce the principle of high resolution melting, point to important aspects in the development of an HRM analysis method, describe how HRM data are analysed and interpreted, continue with a brief overview of its current role in clinical research and diagnostics, and then present and critically discuss the potential of HRM analysis based methods in food analysis. HRM analysis might play a role in the identification of closely related species and cultivars, the identification of pathogenic microorganisms, the screening for genetically modified organisms and the detection of food allergens.

2. High resolution melting (HRM) Double stranded DNA (dsDNA) is very stable at room temperature. However, with increasing temperature the two single strands begin to dissociate until they are completely separated. The

temperature at which 50% of the DNA is single stranded is called melting temperature (Tm). Tm depends on both the length and the guanine-cytosine (GC) content of the DNA fragment. Since GC base pairs are bound by three hydrogen bonds they are more stable than adenine–thymine (AT) base pairs which are linked by only two hydrogen bonds. DNA sequences with a high GC content therefore have a higher Tm than DNA sequences containing a low number of GC base pairs. Melting curve analysis is frequently used in real-time polymerase chain reaction (real-time PCR) in order to check if the correct PCR products (amplicons) have been formed. A prerequisite for carrying out melting curve analysis is the amplification of the template in the presence of a dsDNA binding dye, e.g. SYBR Green. SYBR Green binds to the minor groove of dsDNA independent of its nucleotide sequence. In the unbound state it only exhibits low fluorescence. However, intercalation of SYBR Green molecules between dsDNA results in a dramatic increase of the fluorescence signal. Melting curve analysis is based on gradually increasing the temperature after the last PCR cycle. At the beginning, a high fluorescence signal is obtained due to the high number of (double stranded) amplicons present in the PCR tube. However, at higher temperature the dsDNA dissociates, SYBR Green is released and a decrease of the fluorescence signal is

Fig. 1. Melting curve analysis: melting curve, negative derivative of the fluorescence (F) over temperature (T) ( dF/dT) against the temperature (T).

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observed. The Tm of the amplicon can be determined from the inflection point of the melting curve (Fig. 1) or from the melting peak obtained by plotting the negative derivative of the fluorescence (F) over temperature (T) ( dF/dT) against the temperature (T) (Fig. 1). As already mentioned above, SYBR Green is frequently used in post-PCR melting curve analysis to check the length and the purity of the amplicons. However, melting curve analysis using SYBR Green does not allow discriminating between amplicons showing only small sequence variation. In contrast, an advanced form of melting curve analysis, so-called high resolution melting (HRM) analysis, makes it even possible to detect single-base variants and small insertions or deletions (Reed, Kent, & Wittwer, 2007). The prerequisite for HRM analysis is the use of saturation dyes and high resolution instruments (Erali, Voelkerding, & Wittwer, 2008). In contrast to SYBR Green, saturation dyes do not inhibit the DNA polymerase at rather high concentrations (Vossen, Aten, Roos, & Den Dunnen, 2009). The use of a highly concentrated saturation dye, e.g. LC Green PLUS, Eva Green, SYTO9 or ResoLight, makes it possible to label the PCR product along its entire length, so that all melting domains are detected (Reed et al., 2007). With high resolution instruments a detailed analysis of the melting behaviour, a melting profile, can be obtained due to more precise temperature control, the application of smaller temperature increments (0.01–0.2 °C) and enhanced data acquisition compared to common apparatus. 2.1. Important aspects in the development of an HRM analysis method HRM analysis involves amplification of the target of interest (template) in the presence of a saturation dye by PCR, subsequent melting of the amplicons and finally analysis and interpretation of the data. The specific and efficient amplification of the template is a prerequisite for obtaining reliable and reproducible melting profiles. Primer design and optimisation of the PCR conditions are therefore crucial steps in the development of an HRM based meth-

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od. In designing primers for HRM analysis, one has to follow the general guidelines for primer design, with special emphasis on avoiding the formation of non-specific products, e.g. primer dimers. Since the length of the amplicon influences the sensitivity of subsequent HRM analysis, amplicons should not consist of more than 300 base pairs (bp). The longer the amplicon, the smaller will be the difference in the melting curve caused by a small sequence variation. In addition, long amplicons may contain several melting domains, resulting in rather complex melting profiles. PCR conditions have to be optimised in order to achieve efficient amplification, indicated by low threshold cycles (Ct values) and amplification curves reaching the plateau (Fig. 2). The MgCl2 concentration is one of the parameters that strongly influence the melting behaviour of dsDNA. It has therefore to be optimised very carefully. Other important aspects are the quality and the quantity of the DNA sample. The DNA should be of both high integrity and high purity. Samples should not differ significantly in their DNA amount in order to achieve similar Ct values (Reed et al., 2007; Vossen et al., 2009). 2.2. Data analysis Melting of the amplicons is carried out by gradually increasing (e.g. 0.1 °C/2 s) the temperature after the last PCR cycle. At the end of the melting procedure melting curves are obtained by plotting the fluorescence intensity (F) against the temperature (T). However, in spite of applying optimised PCR conditions and using similar starting amounts of DNA, differences in the fluorescence signal can occur (Fig. 2). These differences are usually accounted for by normalisation of the absolute fluorescent levels obtained before and after melting to 100% and 0%, respectively (Fig. 2). In order to discriminate and highlight small differences between individual melting curves, difference plots are used, obtained by subtracting the sample melting curve from the melting curve of a reference sample (see Fig. 2). In general, complete overlap of a sample curve with the reference curve indicates that

Fig. 2. High resolution melting (HRM) analysis: amplification curves, melting curves before and melting curves after normalisation, difference plot.

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both amplicons have the same sequence. (Two amplicons can, however, have the same melting behaviour although the sequence is not exactly the same, e.g. due to an erasing effect caused by two mutations, e.g. T to G and G to T). Some instruments allow adjusting the temperature axis to overlay the melting curves in a certain fluorescence interval (so-called temperature shifting normalisation). In addition, with some software packages cluster analysis can be performed by grouping samples, based on similarities in the shape of the melting curves, into clusters (Reed et al., 2007).

3. Development and application of HRM analysis methods for food analysis Recent papers indicate that HRM analysis is not only applicable to clinical research and diagnostics but is also an effective tool in food analysis. In the following, we will critically discuss the development and application of HRM analysis methods for food analysis.

3.1. Identification and differentiation of varieties and closely related species

2.3. Current role of HRM analysis in clinical research and diagnostics Since the melting profile of a PCR product depends on its GC content, length, sequence and strand complementarity (heterozygosity), HRM analysis is highly applicable for the detection of sequence variants in clinical research and diagnostics (Reed et al., 2007). Methods allowing the detection of mutations and single nucleotide polymorphisms (SNPs) that are associated with an increased susceptibility and/or progression of diseases are clinically relevant. HRM analysis has been shown to be an efficient tool for gene scanning, the scanning for unknown mutations or SNPs (Erali et al., 2008; Montgomery, Sanford, & Wittwer, 2010; Reed et al., 2007; Vossen et al., 2009). HRM analysis is also suitable for genotyping, the identification and discrimination of homozygous and (known) heterozygous variants (Erali et al., 2008; Montgomery et al., 2010; Reed et al., 2007; Vossen et al., 2009). Melting curves obtained from homozygous variant samples are very similar in shape but are distinguishable from each other by differences in Tm. In contrast, melting curves from heterozygotes display a curve shape distinct from homozygote melting curves because of the formation of two (high melting) homoduplexes and two (low melting) heteroduplexes (base-pairing mismatches). HRM analysis has several advantages over traditional methods for gene scanning and genotyping. In contrast to traditional methods, HRM analysis is performed in the same tube as the amplification step and does not make it necessary to purify or separate the amplicons. This makes HRM analysis faster, less laborious and more suitable for high sample throughput than alternative approaches, e.g. methods based on gel electrophoresis (Wittwer, 2009). In addition, carrying out the analysis in a closed system lowers the risk of contamination. HRM analysis is also less expensive than other approaches, in particular sequencing techniques. Since it is a nondestructive method, subsequent analysis by, for example, gel electrophoresis or sequencing, is still possible if required. However, gene scanning and genotyping by HRM analysis also suffer from some limitations. Different heterozygotes may result in melting curves that can clearly be distinguished from homozygous variants, but are too similar to be differentiated from each other (Wittwer, 2009). In the case of mutation scanning the presence of common SNPs may complicate the interpretation of the results. In addition to gene scanning and genotyping, HRM analysis plays an increasing role in DNA methylation analysis. DNA methylation is an important epigenetic process modulating gene expression. Since methylation of the promoter region of tumour suppressor genes occurs early in carcinogenesis, aberrant DNA methylation patterns are considered as very promising diagnostic and prognostic biomarkers for cancer. Methylation sensitive HRM (MS-HRM) analysis has been found to be a very sensitive tool to determine the extent of methylation in the promoter region of genes. One advantage of MS-HRM is that it allows the identification of heterogeneously methylated samples, accurate quantification of heterogeneous methylation is, however, not possible (Wojdacz, 2012; Wojdacz & Dobrovic, 2009).

To increase their profit, manufacturers may be tempted to incorrectly label their food products and to add lower priced ingredients of inferior quality to more expensive ones. Specific and sensitive analytical methods are necessary to verify the compliance of foods with their declaration in order to protect consumers from fraudulent practises. A broad variety of methods play a role in food authenticity testing, ranging from physical methods, e.g. microscopy, over protein-based techniques to DNA-based methods. Depending on the information one is interested in, an appropriate method has to be selected. Species identification is generally carried out with protein- or DNA-based methods. Due to the instability of proteins, proteinbased methods, e.g. high performance liquid chromatography (HPLC), electrophoresis and immunoanalytical techniques, are only applicable to raw or slightly processed foodstuffs. In contrast, the higher stability of nucleic acids makes it possible to apply DNAbased methods to both raw and highly processed food. A further drawback of protein-based methods is that the protein expression may vary significantly between different tissue types. Most DNAbased methods involve amplification of the target sequence by PCR (Lockley & Bardsley, 2000; Mafra, Ferreira, & Oliveira, 2008). Many of them exploit species specific variations in highly homologous DNA sequences, e.g. microsatellites. PCR-RFLP (restriction fragment length polymorphism) is based on treating the amplicons with restriction endonucleases to obtain species specific restriction fragments that are then separated according to their lengths by gel electrophoresis. In random amplified polymorphic DNA (RAPD) analysis, primers are used that randomly amplify short fragments of the DNA that are subsequently separated by gel electrophoresis. Analysis of single strand conformation polymorphisms (SSCP) involves denaturation of double stranded amplicons to single stranded DNA (ssDNA) that is then subjected to gel electrophoresis. Differences in the secondary structure due to differences in the sequence of the ssDNA result in differences in their electrophoretic mobility. However, since PCR-RFLP, RAPD and PCR-SSCP involve separation of the amplicons by gel electrophoresis, they are rather labour intensive and therefore not very suitable for high throughput analysis. In contrast, alternative approaches such as real-time PCR using species specific, fluorescent labelled probes and automated sequencing methods are less laborious but rather costly (Lockley & Bardsley, 2000). Recent papers show that HRM analysis is a very cost efficient high throughput method for the identification and differentiation of cultivars and closely related species (Bosmali, Ganopoulos, Madesis, & Tsaftaris, 2012; Distefano, Caruso, La Malfa, Gentile, & Wu, 2012; Ganopoulos, Argiriou, & Tsaftaris, 2010, 2011; Ganopoulos, Bosmali, Madesis, & Tsaftaris, 2012; Ganopoulos, Madesis, Darzentas, Argiriou, & Tsaftaris, 2012; Jaakola, Suokas, & Haeggman, 2010; Mackay, Wright, & Bonfiglioli, 2008; Madesis, Ganopoulos, Anagnostis, & Tsaftaris, 2012; Sakaridis, Ganopoulos, Argiriou, & Tsaftaris, 2013). Table 1 gives an overview of the HRM analysis methods published. The methods can be divided into two groups, targeting either microsatellites (Bosmali et al., 2012; Distefano et al., 2012; Ganopoulos et al., 2010; Ganopoulos et al., 2011;

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B. Druml, M. Cichna-Markl / Food Chemistry 158 (2014) 245–254 Table 1 Overview of HRM analysis methods applied in food analysis. Aim

Target region

Application

Reference

Verification of the identity of grapevine cultivars Detection of Basmati rice adulteration Differentiation of Citrus species and hybrids

Microsatellites (VVS2, VVMD5, VVMD7, VVMD24, VVMD27, VVMD32, VVMD26, ZAG62, ZAG79) Microsatellites (RM1, RM72, RM206, RM241, RM287), BADEX7-5 Microsatellites (SSR16, SSR21, SSR34, SSR92, SSR93, SSR115, SSR116, SSR137, SSR203, SSR338, SSR430, SSR482, SSR818, SSR1210, SSR1388, SSR1527) Microsatellites (EMPA004, EMPA005, EMPA015, EMPA018, EMPAS01, EMPAS02, EMPAS10, EMPAS11, EMPAS12, UDP98-412) Microsatellites (SSR33, SSR156, SSR323, SSR107, SSR48) rpoC1 chloroplast region Microsatellites (BM139, BM172, BM189, BM210)

Rootstock and grapevine scion varieties commonly grown in New Zealand Five Basmati and three non-Basmati rice cultivars, seven commercial rice products Fifteen genotypes belonging to eleven citrus species and hybrids

Mackay et al. (2008) Ganopoulos et al. (2011) Distefano et al. (2012)

Four commercial jams and one biscuit product

Ganopoulos et al. (2010)

Ten Greek lentil varieties

Bosmali et al. (2012)

Thirty cultivars growing in Greece and neighbouring areas

Ganopoulos, Bosmali, et al. (2012)

Internal transcribed spacer (ITS) and plastid DNA (rpl36-rps8, trnL-F, trhH-psbA) trnL and rpoC chloroplast regions

Berries from the plant families Ericaceae, Crossulariaceae, Caprifoliaceae and Rosaceae Ten commercial products (one ‘‘Fava Santorinis’’, three putative ‘‘Fava Santorinis’’ and five ‘‘Fava’’ products)

Jaakola et al. (2010) Ganopoulos, Madesis, et al. (2012) Madesis et al. (2012) Sakaridis et al. (2013) Wang et al. (2010) Jin et al. (2012) Pietzka et al. (2011)

Verification of the authenticity of PDO sweet cherry cultivar ‘‘Tragana Edessis’’ Verification of the authenticity of PGI lentil cultivar ‘‘Eglouvi’’ Verification of the authenticity of PGI common bean cultivar ‘‘Plake Megalosperma Prespon’’ Differentiation of bilberry from other berry species Verification of the authenticity of PDO ‘‘Fava Santorinis’’ Verification of the authenticity of Leguminosae Detection of bovine milk in buffalo milk products Identification of Listeria species

trnL chloroplast region

Thirteen Leguminosae species

12S rRNA gene, bovine mitochondrial D-loop

Nine commercial buffalo dairy products

Intergenic spacer region of the rRNA gene

207 Listeria isolates

Identification of Listeria species

ssrA gene

Serotyping of Listeria monocytogenes

Internalin B gene

Serotyping of Salmonella spp.

Clustered, regularly interspaced short-pallindromicrepeats (CRISPR)

53 Listeria and 45 non-Listeria species; 30 artificially contaminated food samples 172 Clinical L. monocytogenes isolates, 20 isolates from culture collections; 100 food isolates collected during a multinational outbreak of listeriosis in 2009 and 2010 49 Salmonella spp. strains

Genotyping of Staphylococcus aureus Differentiation of Bacillus cereus group isolates Differentiation of Escherichia coli strains Screening for GMOs

Six DNA fragments containing SNPs

94 S. aureus isolates

16S-23S ISR, 5S-GT, 16S-ISR

Seven Bacillus strains

stx1, stx2, eae genes P-35S, T-NOS, ColE1 (as control)

Artificially inoculated food matrices (minced meat, salad, milk) GM maize, GM potato, biscuit, rice

Screening for GMO rice

P-35S, T-NOS, (Bt) CryIAb/Ac

GM rice, GM maize

Detection of potentially allergenic almonds

60A acidic ribosomal protein gene

30 processed food samples

Ganopoulos, Bosmali, et al., 2012; Mackay et al., 2008) or DNA barcoding regions (Ganopoulos, Madesis, et al., 2012; Jaakola et al., 2010; Madesis et al., 2012; Sakaridis et al., 2013). In 2008, Mackay et al. (2008) presented an HRM based method for verifying the identity of grapevine cultivars. They targeted various microsatellites (VVS2, VVMD5, VVMD7, VVMD24, VVMD27, VVMD32, VVMD26, ZAG62 and ZAG79) that, according to previous studies, are suitable for the differentiation of grapevine cultivars. The microsatellites selected are dinucleotide repeat sequence motifs with allele sizes ranging from 129 bp to 315 bp. Mackay et al. developed a real-time PCR protocol that allows amplifying all microsatellites in one and the same run. They showed that a number of closely related grapevine rootstocks, particular those of industrial use in New Zealand, can be distinguished by HRM analysis at one single microsatellite marker. HRM analysis at microsatellite locus VVMD32 even allowed to distinguish two rootstocks that share one allele and differ by only 2 bp (a single CT dinucleotide repeat) at the other allele. Identification of grapevine cultivars was possible by comparing the melting curve obtained for an

Bratcˇikov and Mauricas (2009) Lilliebridge et al. (2011) Antolinos et al. (2012) Kagkli et al. (2012) Akiyama et al. (2009) Kluga et al. (2013) Costa et al. (2012)

unknown cultivar with those of known (reference) cultivars. According to the authors, the identity of an unknown grapevine cultivar can be verified within 3 h. The HRM method is thus significantly faster than alternative methods for microsatellite analysis (Mackay et al., 2008). Basmati rice is a rice cultivar famous for its unique fragrance. Because of its rather high price, food producers may be tempted to adulterate Basmati rice by adding cheaper non-Basmati rice cultivars. Ganopoulos et al. (2011) demonstrated the applicability of HRM analysis for the detection of Basmati rice adulteration. HRM analysis at five microsatellite markers known from literature (RM1, RM72, RM206, RM241, RM287) was tested for its suitability to differentiate between five Basmati and three non-Basmati rice cultivars. Among the microsatellite markers tested, RM241 was found to be applicable for distinguishing most of the rice cultivars. HRM analysis at microsatellite marker RM1 even allows differentiating two Basmati rice cultivars in spite of the same allele size (112 bp). The HRM analysis method was tested for its applicability to verify the presence of Basmati rice cultivars in commercial rice

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products. The shape of the HRM curves even indicated if one or more than one Basmati rice cultivar was cointained in the food product. In the same paper, another HRM analysis method, targeting the marker BADEX7-5, was presented. It allows distinguishing fragrant rice genotypes from non-fragrant ones. The method is based on the fact that a single allele, badh2.1, of the gene encoding the BAD2 (betaine aldehyde dehydrogenase homologue 2) enzyme is the predominant allele in virtually all fragrant rice varieties. Fragrant rice genotypes (95 bp) differ from non-fragrant ones (103 bp) due to an 8 bp deletion in the badh2.1 allele. Ganopoulos et al. showed that the eight rice cultivars and the seven commercial rice products could be assigned correctly to fragrant (melting at lower temperature) and non-fragrant (melting at higher temperature) genotypes by HRM analysis (Ganopoulos et al., 2011). Distefano et al. (2012) applied HRM analysis in order to differentiate Citrus species and hybrids. Sixteen microsatellite markers (SSR16, SSR21, SSR34, SSR92, SSR93, SSR115, SSR116, SSR137, SSR203, SSR338, SSR430, SSR482, SSR818, SSR1210, SSR1388, SSR1527) were used. Ten of the 16 microsatellites contained SNPs in the flanking region of the nucleotide repeats. Distefano et al. analysed 15 Citrus species and hybrids by HRM analysis and compared the results with those obtained by capillary electrophoresis (CE). In the case of the microsatellite markers that did not contain SNPs in the flanking region, the same genotypes were detected by HRM analysis and CE. However, in the case of the ten markers containing SNPs in the flanking sequences, more genotypes were detected by HRM analysis than by CE. These results are caused by the fact that by CE only genotypes with length polymorphism are distinguished whereas HRM allows detecting nucleotide variations such as SNPs. In total, 66 alleles were detected by HRM analysis and 48 by CE. In some cases, different genotypes resulted in very similar normalised melting curves. However, difference plots obtained by subtracting sample melting curves from the melting curve of a reference sample, made it possible to distinguish these genotypes. Both HRM analysis and CE allowed the discrimination of most of the 15 Citrus genotypes, with the exception of those belonging to the same species (three sweet orange and three clementine cultivars) (Distefano et al., 2012). The origin of food products may play a role in the consumers’ decision to buy the foodstuffs. In the European Union (EU) two schemes have been established to protect food products from a certain geographical area from misuse and imitation, namely ‘‘protected designation of origin (PDO)’’ and ‘‘protected geographical indication (PGI)’’. PDO products must be produced, processed and prepared in a given geographical area. For PGI products it is mandatory that at least one of the stages of production, processing or preparation is carried out in the given area (Profeta, Balling, Schoene, & Wirsig, 2010). Ganopoulos et al. (2010) presented an HRM analysis method for the differentiation of sweet cherry cultivars. They aimed at verifying the authenticity of the PDO sweet cherry cultivar ‘‘Tragana Edessis’’ in sweet cherry containing products. Results obtained by HRM analysis were compared with those obtained by CE. By testing ten microsatellites, six of them (EMPA004, EMPA005, EMPA015, EMPA018, EMPAS01, EMPAS02) were found to allow the discrimination of thirteen sweet cherry cultivars in HRM analysis. Five microsatellites (EMPAS02, EMPAS10, EMPAS11, EMPAS12 and UPD98-412) made it possible to identify the PDO cultivar in products containing sweet cherry by both HRM analysis and CE. Results obtained by the analysis of four commercial jams and one biscuit product indicated that HRM can be used to assess if a single known cultivar or more than one was used during the process of the product (Ganopoulos et al., 2010). In another study, Ganopoulos and co-workers (2012) pursued a very similar strategy in the development of an HRM based method for the identification of lentil cultivars. Five microsatellite markers

(SSR33, SSR156, SSR323, SSR107, SSR48) were used to identify the PGI lentil cultivar ‘‘Eglouvi’’. Unique melting curves were obtained for each of ten Greek lentil cultivars. Among the microsatellites tested, SSR156 was found to be the most and SSR107 the least discriminative marker (Bosmali et al., 2012). The same research group (Ganopoulos, Bosmali, et al., 2012) published a microsatellite HRM analysis method for the identification of the PGI common bean variety ‘‘Plake Megalosperma Prespon’’. By using a set of four microsatellite markers (BM139, BM172, BM189, BM210) the PGI common bean cultivar could be distinguished from 29 cultivars growing in Greece and neighbouring areas (Ganopoulos, Bosmali, et al., 2012). In the following studies HRM analysis was not based on microsatellite markers but on DNA barcoding regions. DNA barcoding is a relatively new approach for the detection of food adulteration. It is based on the analysis of the variability within a standard region of the genome called ‘‘DNA barcode’’ (Hebert, Cywinska, Ball, & de Waard, 2003). In order to allow the identification of different taxa, DNA barcodes should be of high taxonomic coverage (the sequence should be amplified in all taxa of interest) and should have high interspecific but low intraspecific variability (Galimberti et al., 2013). Plant researchers have proposed a variety of barcode regions including coding (e.g. rbcL, matK, rpoB) and non-coding ones (e.g. the nuclear internal transcribed spacer (ITS) region and the plastid trnH-psbA intergenic spacer) (Fazekas et al., 2008). Ribosomal RNA (rRNA) genes are considered as suitable DNA barcodes for the animal kingdom (Chu, Li, & Qi, 2006). Jaakola et al. (2010) developed an HRM analysis method targeting DNA barcoding regions for verifying the authenticity of berry species, in particular for distinguishing bilberry (Vaccinium myrtillus L.) from other berry species of similar colour from the plant families Ericaceae, Crossulariaceae, Caprifoliaceae and Rosaceae. They started with sequencing standard regions of the DNA, the internal transcribed spacer (ITS) as well as plastid DNA (rpl36-rps8, trnL-F and trhH-psbA), of eight wild berry species. Primers were designed and used for amplifying the selected sequences. HRM analysis of the amplicons (188–359 bp) was found to be suitable for the identification of more or less closely related wild and cultivated berry species. When a set of primer pairs was used all berry species could be distinguished from each other. Melting curves obtained for DNA extracts from dried fruit tissue did not differ significantly from those obtained with extracts from unprocessed ones. The method can therefore be applied to processed berry products such as jams, jellies or juices (Jaakola et al., 2010). In one of the studies already mentioned above, Ganopoulos et al. targeted the species specific rpoC1 chloroplast region in order to distinguish lentil species from vetch (Vicia sativa) which is a frequent contaminant of lentils (Bosmali et al., 2012). The same research group (Ganopoulos, Madesis, et al., 2012) presented an HRM analysis method targeting the plant DNA barcoding regions trnL and rpoC in order to verify the identity of PDO ‘‘Fava Santorinis’’. The aim was to distinguish PDO ‘‘Fava Santorinis’’ from legume species (four Lathyrus, two Vicia and two Pisum species) that could be used as adulterants in commercial products declared to contain PDO ‘‘Fava Santorinis’’. Differences in the melting curves at the trnL region allowed discriminating the eight species analysed in the study. For two closely related species the melting curves were very similar, these species could, however, be distinguished with the help of a difference plot. The rpoC chloroplast region was used to detect ‘‘Fava Santorinis’’ adulterants in ten commercial products comprising one original ‘‘Fava Santorinis’’, three putative ‘‘Fava Santorinis’’ and five ‘‘Fava’’ products. The results indicated that 75% of the commercial products were adulterated (Ganopoulos, Madesis, et al., 2012). In the same year, the research group of Ganopoulos (Madesis et al., 2012) published an HRM analysis method for the

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identification of 13 Leguminosae species. HRM analysis at the trnL region allowed distinguishing the 13 species investigated. Sequencing revealed that the differences in the melting curves were caused by deletions, insertions and a number of SNPs in the trnL region (Madesis et al., 2012). Very recently, a further HRM analysis method was developed by the same research group (Sakaridis et al., 2013), allowing the detection of bovine milk in buffalo milk products. Amplification of two target sequences was carried out in a duplex PCR assay. One primer pair targeted the buffalo 12S rRNA gene, the other one the bovine mitochondrial D-loop. The method was applied to detect bovine milk in nine commercial buffalo dairy products, e.g. buffalo butter and buffalo yoghurt (Sakaridis et al., 2013). In some of their studies Ganopoulos et al. reported that HRM analysis allows assessing the amount of adulterant in a sample (Ganopoulos et al., 2011; Ganopoulos, Bosmali, et al., 2012; Ganopoulos, Madesis, et al., 2012; Madesis et al., 2012; Sakaridis et al., 2013). In their study on the detection of Basmati rice adulteration, for example, mixtures of non-Basmati rice and Basmati rice in proportions from 1% to 50% were subjected to HRM analysis (Ganopoulos et al., 2011). According to the authors the HRM analysis method allows the detection of 1% non-Basmati rice in Basmati rice. The same limit of detection was reported for the detection of vetch in lentils (Bosmali et al., 2012), the detection of ‘‘Fava Santorinis’’ in mixtures with Lathyrus cicera (Ganopoulos, Madesis, et al., 2012), the detection of Lupinus spp. in Glycine max flour (Madesis et al., 2012) and the detection of bovine milk in buffalo milk products (Sakaridis et al., 2013). In order to be able to determine the amount of a given adulterant in a sample, standard curves were established by plotting the fluorescence level against the percentage of the adulterant. In all studies a correlation was found between the fluorescence level and the amount of adulterant in the mixture (Ganopoulos et al., 2011; Ganopoulos, Bosmali, et al., 2012; Ganopoulos, Madesis, et al., 2012; Madesis et al., 2012; Sakaridis et al., 2013). However, in all cases the standard curve was established by linear regression analysis in spite of the fact that an optical evaluation of the data points hardly justifies that there is a linear relationship (see in particular Ganopoulos et al., 2011; Ganopoulos, Bosmali, et al., 2012; Ganopoulos, Madesis, et al., 2012). In our opinion, HRM analysis methods have to be validated in more detail in order to demonstrate their applicability for the accurate quantification of adulterants. So far, a critical discussion of important issues, e.g. the influences of food processing and matrix effects on the sensitivity and accuracy of the methods, is missing. 3.2. Genotyping and serotyping of pathogenic microorganisms Most foodborne diseases are caused by the consumption of food containing pathogenic microorganisms. In general, contamination with pathogenic microorganisms occurs due to poor foodhygiene practises. Contamination can happen at any step along the food chain, from the production over processing and distribution to the preparation. In the case of a foodborne disease outbreak, the pathogenic microorganisms have to be identified at the species and sub-species level in order to elucidate the route and source of contamination and to select an adequate treatment regime (Settanni & Corsetti, 2007). Traditional culture-based methods are not optimal for the identification of species and sub-species because they are very laborious and time consuming. In addition, stressed or weakened bacteria cells often need specific culture conditions to become cultivated. For these reasons, culture-independent methods have been developed, including RFLP, SSCP, PCR-DGGE (denaturing gradient gel electrophoresis), pulsed-field gel electrophoresis (PFGE) and multiplex real-time PCR. With the exception of multiplex real-time PCR, all methods

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rely on post PCR separation by gel electrophoresis. PCR-DGGE is based on amplifying the target sequence followed by separating the amplicons on an acrylamide gel having a low to high gradient of a denaturant, e.g. formamide or urea. DGGE allows separating DNA fragments of similar length but differing in their sequence according to their melting properties. In general, DNA fragments differing in only a few base pairs can be separated. However, it can happen that different fragments have the same electrophoretic mobility and thus co-migrate. PFGE makes it possible to separate even large DNA sequences by applying an electric field that periodically changes the direction to the gel matrix. At present, PFGE is considered the gold standard for the identification of species and subspecies of food-related pathogenic microorganisms (Pietzka, Stoeger, Huhulescu, Allerberger, & Ruppitsch, 2011). A major drawback of both DGGE and PFGE is, however, the difficulty of standardization (Pietzka et al., 2011). Multiplex real-time PCR is a very efficient technique because it allows the detection of several pathogenic microorganisms simultaneously in one and the same well (Germini, Masola, Carnevali, & Marchelli, 2009). Genotyping by next generation sequencing methods seems to be very promising and will probably play an important role in the future. Recent papers indicate that HRM analysis is a rather costly alternative, allowing the accurate identification of food-related pathogenic microorganisms at the species and strain level (Antolinos, Fernandez, Ros-Chumillas, Periago, & Weiss, 2012; Bratcˇikov & Mauricas, 2009; Jin et al., 2012; Kagkli, Folloni, Barbau-Piednoir, Van den Eede, & Van den Bulcke, 2012; Lilliebridge, Tong, Giffard, & Holt, 2011; Pietzka et al., 2011; Wang, Yamada, & Ohashi, 2010). Three of the papers deal with the identification and serotyping of Listeria species (Jin et al., 2012; Pietzka et al., 2011; Wang et al., 2010). The genus Listeria contains nine species (L. monocytogenes, L. welshimeri, L. seeligeri, L. ivanovii, L. innocua, L. grayi, L. marthii, L. rocourtiae and L. fleischmannii), the last three were identified recently. Among these species, only L. monocytogenes causes listeriosis, a serious infection affecting particularly neonates, pregnant women, adults with weakened immune system and elder individuals. Only three of the 13 serotypes of L. monocytogenes identified account for more than 96% of reported human listeriosis cases in Austria (Pietzka et al., 2011). Wang et al. (2010) presented an HRM analysis method for the identification and screening of variants of Listeria species. They targeted the large intergenic spacer region of the rRNA gene. In total, 207 Listeria isolates including 95 L. monocytogenes isolates were used. Amplification by PCR resulted in amplicons of 343–374 bp. HRM analysis of the amplicons yielded eleven different HRM curves and made it possible to classify the isolates into six Listeria species. Three different melting curves were obtained for the 95 L. monocytogenes isolates. Sequencing of the amplicons revealed that the differences in the HRM profiles were caused by differences in the nucleotide sequence. HRM did, however, not allow distinguishing between L. monocytogenes serotypes because strains of different serotypes resulted in identical melting curves (Wang et al., 2010). Another HRM analysis method for the identification of six Listeria species was published by Jin et al. (2012). They selected a 166 bp fragment of the ssrA gene, which encodes a transfer-messenger RNA (tmRNA). In total, 53 Listeria species (including 34 L. monocytogenes and L. innocua strains) and 45 non-Listeria species were analysed. HRM analysis allowed the identification of Listeria species with a specificity of 100% in reference to conventional methods. The method was applied to artificially contaminated food samples (juice, milk, cheese and meat) prepared by directly spiking them with Listeria species. In 28 samples the Listeria species were correctly identified. Identification of the species failed, however, in two juice samples (Jin et al., 2012).

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Pietzka et al. (2011) published an HRM analysis method applicable for serotyping of L. monocytogenes. They targeted a 500-bp sequence of the Virulence gene internalin B (inlB). In total, 172 clinical L. monocytogenes isolates comprising 11 different serotypes and 20 reference strains of defined serotypes were analysed. HRM analysis of the amplicons obtained from the 192 isolates yielded 15 different melting curves. Sequence analysis revealed that these 15 melting curves originated from 18 different inlB sequence types. The HRM analysis method made it possible to classify the three most frequent serotype isolates into 13 distinct subgroups. Some serotypes of minor relevance could, however, not be distinguished. The HRM analysis method was used for screening more than 100 food isolates during a multinational outbreak of listeriosis in 2009 and 2010 in Austria, Germany and the Czech Republic. The HRM curve profiling allowed unambiguous differentiation between the two outbreak clones (Pietzka et al., 2011). Bratcˇikov and Mauricas (2009) presented an HRM analysis method for serotyping of Salmonella spp. Salmonellosis is one of the most common and widely spread foodborne diseases. The great majority of these infections is caused by the consumption of products such as meat, poultry, eggs and milk (Foley, Lynne, & Nayak, 2008). Bratcˇikov and Mauricas targeted two separate regions of CRISPRs, CRISPR1 and CRISPR2, in order to differentiate different serotypes. CRISPRs (clustered, regularly interspaced short palindromic repeats) contain short direct repeats, intermitted by nonrepetitive spacer sequences of similar length. By analysing 49 strains of Salmonella spp., 46 genotypes were identified, 23 genotypes for each CRISPR. The genotypes of both CRISPRs were related to the same Salmonella spp. serotypes (Bratcˇikov & Mauricas, 2009). Lilliebridge et al. (2011) applied HRM analysis for Staphylococcus aureus genotyping. The toxins produced by S. aureus, a commonly found bacterium on the skin of humans, are frequently responsible for food poisoning. The HRM analysis method target six different DNA fragments containing SNPs. The length of the amplicons ranged from 83 to 219 bp. HRM analysis of 94 S. aureus isolates yielded 268 melting types (Lilliebridge et al., 2011). Antolinos et al. (2012) developed an HRM based approach for the differentiation of Bacillus cereus group isolates. The B. cereus group includes six different species, including B. thuringiensis, B. weihenstephanensis and B. cereus sensu stricto, a frequently cause for food poisoning. Members of the B. cereus group are known to show high polymorphisms in the 16S and 23S ISR region and intra-species variations in rRNA operon genes. HRM analysis at the selected 16S-23S ISR region did not allow species differentiation, it was, however, found to be applicable for B. cereus sensu stricto strain typing. In contrast, two other polymorphic regions within the bacterial rRNA operon (5S-GT and 16S-ISR) allowed the differentiation between Bacillus species (Antolinos et al., 2012). Escherichia coli is a heterogeneous group of typically non-pathogenic bacteria that are naturally present in the intestinal flora of animals and human. Five major groups of pathogenic E. coli have already been identified: enteropathogenic (EPEC), enteroaggregative (EAEC), enterotoxigenetic (ETEC), enteroinvasive (EIEC) and verocytotoxin or shigatoxin producing E. coli (VTEC or STEC, respectively). Kagkli et al. (2012) developed an HRM analysis method targeting the genes encoding Shiga toxin 1 (stx1), Shiga toxin 2 (stx2) and the virulence factor intimin (eae). The authors demonstrated the applicability of HRM analysis to discriminate between strains based on the toxin variant they possess. All results obtained by HRM analysis were verified by sequencing analysis. In addition, the authors showed that not only singleplex PCR, but also multiplex PCR can be coupled with HRM analysis. In this case, optimisation of the PCR conditions is, however, very crucial, to amplify all targets with similar efficiency (Kagkli et al., 2012).

3.3. Screening for genetically modified organisms (GMO) Organisms are genetically modified in order to confer them a certain trait. Most frequently the genetic modification is based on the introduction of a gene (from another species) that encodes the protein conferring the certain benefit. Plants are predominantly modified in order to make them tolerant for herbicides and/or resistant to insects. According to the last ISAAA (International Service for the Acquisition of Agri-Biotech Applications) report 29 commercialised genetically modified (GM) crops have been planted in 29 countries in 2010. So far, 196 GM events, representing 25 crops, have been approved worldwide (James, 2011). The approval of GM crops is regulated in different countries by respective legal frameworks. Several countries have implemented mandatory labelling for foods derived from transgenic plants (Elenis, Kalogianni, Glynou, Ioannou, & Christopoulos, 2008). In the EU, all food or feed products that consist, contain or are made from GMOs are subject to the labelling obligation. Traces of GMOs that do not exceed the threshold of 0.9% and those presence is adventitious and technically unavoidable are, however, exempted from this obligation. Analytical methods are necessary in order to verify if the food products comply with the labelling regulations. In principle, protein-based methods targeting the expressed protein, e.g. immunoassays and lateral flow devices, can be applied. However, DNA-based methods, most frequently based on PCR, play by far the most important role in GMO analysis. In general, the DNA construct inserted into the plant genome consists of a transcription promoter, a coding sequence and an expression terminator. Many transgenic plants contain the promoter of the 35S subunit of ribosomal RNA (P-35S) of the Cauliflower Mosaic Virus and the terminator of the nopaline synthase gene (T-NOS) from Agrobacterium tumefaciens. In routine analysis, PCR methods differing in their selectivity are combined. First of all, screening methods are applied, targeting frequently used genetic elements such as P-35S and T-NOS (Waiblinger, Grohmann, Mankertz, Engelbert, & Pietsch, 2010). In the case of a positive result, the GM event has to be identified by product or event specific PCR methods. These methods are specific for the detection of the altered target gene sequence or target the boundary of two adjacent genetic elements (e.g. the promoter and the target gene), respectively. If the GMO has been approved, finally its concentration has to be determined in order to assess if the concentration is P0.9%. Two recent papers report the application of HRM analysis in GMO analysis (Akiyama et al., 2009; Kluga et al., 2013). In both papers screening methods based on HRM analysis are presented. Akiyama et al. (2009) developed a multiplex real-time PCR–HRM analysis method for the simultaneous detection of P-35S and TNOS in crops. In addition to the primer pairs for P-35S and TNOS, a third primer pair was designed for ColE1, a naturally occurring plasmide of E. coli, that was spiked to the samples in order to evaluate the performance of PCR. The amplicons obtained with the three primer pairs differed in their size and sequence and could therefore be distinguished by HRM analysis. Melting curves of amplicons obtained by multiplex PCR did not differ significantly from curves of amplicons obtained by singleplex PCR. The limit of detection of the method was determined by analysing certified reference materials of NK603 maize samples containing from 0.1% to 5% GMO. The method was found to allow the detection of 0.1% GMO in the reference material. In order to assess the applicability of the method to other crops, an authorised GM potato, a biscuit contaminated with GM soybean and a rice sample contaminated with unauthorised GM rice were analysed. HRM analysis resulted in melting curves containing peaks for P-35S and T-NOS in GM soybean, GM potato as well as the biscuit and

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the rice containing GMOs. P-35S and T-NOS were not detected in non-GM samples (Akiyama et al., 2009). Kluga et al. (2013) published an HRM analysis based screening method for the detection of GM rice. They targeted P-35S, T-NOS and the coding region of Bacillus thuringiensis (Bt) CryIAb or CryIAc insect resistance genes. Amplicons obtained from the GM rice Bt63 and Kefeng6 and the GM maize Bt11 and MON810 were subjected to HRM analysis. Genomic DNA from GM maize Bt11 and MON810 were used as references for CryIAb1 and 2, respectively, and the GM cotton MON 531 as a reference for CryIAc. HRM analysis indicated that the Bt targets in Bt63 and Kefeng6 are slightly distinct but very related to the CryIAb target present in Bt11 maize (Kluga et al., 2013). These two papers indicate that HRM analysis can be applied for the screening of genetic elements frequently found in transgenic plants. However, due to the use of alternative genetic elements and the increasing number of commercially available GM crops only part of the GM plants will be detected by this approach. Waiblinger et al. (2010) have recently shown that targeting a set of five genetic elements would significantly increase the efficiency of the screening approach. In our opinion, more studies demonstrating the potential of HRM analysis have to be carried out before HRM analysis can be considered as an alternative approach to realtime PCR using fluorescently labelled probes in GMO analysis. 3.4. Detection of food allergens Food allergies are immunological adverse reactions to food. About 3% of the adults and 6–8% of children are affected by food allergies (Sampson, 2004). The prevalence of food allergies differs from country to country and depends on genetic factors, cultural and dietary habits and in particular on the time of exposure to the certain food product in life. Allergic patients suffer from a variety of symptoms, including reactions affecting the skin, the gastrointestinal tract and/or the respiratory system. However, the consumption of allergenic food can also lead to life-threatening reactions like anaphylaxis. So far, there is no cure for food allergies. The only option for allergic patients is therefore to strictly avoid the consumption of the food they are allergic to. In order to protect allergic individuals, 14 allergenic foods and products thereof have to be declared in the European Union according to the Directive 2007/68/EC (Commission of the European Union, 2007). Analytical methods applied in food allergen analysis should fulfill several criteria. They should be specific for the given allergenic food in order to avoid false positive results. Since allergens are most frequently present in rather low concentrations, the limit of detection should be about 10 ppm in order to avoid false negative results. In addition, the methods should allow the detection of the allergen in raw but also in highly processed foods. In general, the analytical methods applied in food allergen analysis can be divided into three groups, immunoanalytical methods including immunoassays and lateral flow devices, DNA-based methods, most frequently relying on amplification by PCR, and methods based on mass spectrometry. The advantages and disadvantages of these methods have been critically discussed in several review articles (Monaci & Visconti, 2010; van Hengel, 2007). Very recently, Costa, Mafra, and Oliveira (2012) presented a novel approach for the detection of food allergens based on HRM analysis. The authors aimed at detecting potentially allergenic almond (Prunus dulcis) in foods. Almond belongs to the Rosaceae family and is closely related to fruits such as peach, plum, apples and cherries. The method targets the P. dulcis 60A acidic ribosomal protein gene encoding the allergen Pru du 5. Melting curves obtained for 12 almond cultivars could be distinguished from those obtained for other plant foods, including the closely related fruits

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from the Rosaceae family. Analysis of almond mixtures containing 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 5% or 10% almond in walnut revealed that almond could be detected down to the level of 0.005% (w/w). The method was applied to 30 processed food samples containing almond and/or other tree nuts, including nut snacks, cereal foods, biscuits, cake and chocolate bars. Almond reference mixtures and all the processed samples with previous positive amplification for almond were grouped in one cluster, whereas Macadamia nut, walnut and hazelnut were included in different clusters. In a walnut cake sample almond was detected although it was not declared. Sequencing analysis of the PCR products obtained for almond and related species confirmed the results obtained by HRM analysis (Costa et al., 2012). Costa et al. have pursued a novel and interesting strategy in food allergen analysis. More research is definitely necessary in order to be able to assess the applicability of HRM analysis in food allergen analysis. The performance of HRM analysis methods should be validated in detail and compared with that of well established methods like immunoassays, real-time PCR with fluorescent probes and mass spectrometry. 4. Conclusion HRM analysis is an advanced form of melting curve analysis. After amplifying the DNA sequence of interest in the presence of a saturation dye, the temperature is increased in rather small increments. In contrast to traditional melting curve analysis HRM analysis not only makes it possible to determine the melting temperature but also the melting profile of an amplicon. Since the melting profile of a PCR product depends on its GC content, length, sequence and strand complementarity (heterozygosity), HRM analysis is highly suitable for the detection of SNPs and mutations and therefore plays an important role in clinical research and diagnostics. Recently, several papers have reported the application of HRM based methods in food analysis. Most of the papers deal with the identification and differentiation of closely related species and cultivars in order to detect food adulteration. These studies demonstrate that HRM analysis method is an efficient tool to distinguish between closely related species and cultivars. The identity of samples can be verified by comparing the melting curves with those of reference samples. If the amplicon contains several melting domains, the melting curves can, however, be rather complex. In addition, melting curves obtained for different genotypes can be very similar. In these cases, difference plots, obtained by subtracting sample melting curves from the melting curve of a reference sample, may help. In contrast to other frequently used techniques for genotyping, such as PCR-RFLP, RAPD and PCR-SSCP, HRM analysis does not make it necessary to carry out post-PCR processing steps. It is therefore more suitable for high-throughput analysis and less prone to contaminations. Although some papers report that HRM analysis can be used to quantify the adulterant, more detailed validation experiments must be carried out to demonstrate that the results obtained are accurate. A number of papers have shown that HRM analysis can also be applied for genotyping and serotyping of foodborne related pathogenic microorganisms. The discriminatory power of HRM analysis is very high but lower than that of PFGE, the gold standard for genotyping and serotyping of bacteria. However, since HRM analysis is less time-consuming, it might be an additional screening tool in the case of an outbreak situation. So far, only a few papers investigated the applicability of HRM analysis for screening of GMOs and the detection of food allergens. These approaches are very interesting. However, in further studies the performance of HRM analysis has to be compared to the

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performance of methods that have already been validated in detail and are well established in these fields.

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