The German National Reference Centre for Authentic Food (NRZ-Authent) and the competent German food control authorities of the federal states cooperated within the framework of the 10th joint Europol INTERPOL operation OPSON (OPSON X) in the detection of adulterated meat products. A total of 63 meat product samples were collected and analysed by the authorities using standard analytical procedures and subjected to a recently published 16S rDNA metabarcoding analysis. The sequence reads were analysed using 3 bioinformatics data processing strategies. The study aimed to gain additional data on the test samples regarding the authenticity of the declared species and to validate the 16S rDNA metabarcoding method with representative samples. The method was tested not only on 63 test samples, but also on 5 commercial samples from 2 interlaboratory comparison studies and 9 mock mixtures in parallel. The 16S rDNA metabarcoding method was able to detect species that were not target species of the used standard analytical methods, but failed, as shown previously, to detect fallow deer. Otherwise, the qualitative results of the 16S rDNA metabarcoding method were very similar to those of the methods currently in use by the German food control laboratories. Thus, the method has great potential to be used as a screening method for the authentication of mammal and poultry species in meat products.

LebensmittelchemieVolume 76, Issue S2 p. S2-179-S2-179 Authentizität (AUT) Metabarcoding zur Überprüfung der Lebensmittelauthentizität und -sicherheit Larissa Murr, Larissa Murr Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorMelanie Pavlovic, Melanie Pavlovic Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorNancy Bretschneider, Nancy Bretschneider Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorMarzena Maggipinto, Marzena Maggipinto Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorLars Gerdes, Lars Gerdes Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorUlrich Busch, Ulrich Busch Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorIngrid Huber, Ingrid Huber Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this author Larissa Murr, Larissa Murr Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorMelanie Pavlovic, Melanie Pavlovic Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorNancy Bretschneider, Nancy Bretschneider Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorMarzena Maggipinto, Marzena Maggipinto Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorLars Gerdes, Lars Gerdes Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorUlrich Busch, Ulrich Busch Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this authorIngrid Huber, Ingrid Huber Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, D-85764 OberschleißheimSearch for more papers by this author First published: 01 September 2022 https://doi.org/10.1002/lemi.202259135AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume76, IssueS2September 2022Pages S2-179-S2-179 RelatedInformation

Background: Firm raw sausages or ham from game species like chamois, red deer, or roe deer are typical meat products from the alpine regions in Germany, Italy, Austria, and Switzerland. Increased hunting effort and limited numbers make chamois meat more expensive than comparable game meat. In routine analysis at Bavarian Health and Food Safety Authority (LGL), screening of meat relevant animal species is usually performed by LCD array. However, chamois is not included on the commercial array. A simple alternative method for chamois detection in mixed products was therefore required. Results: We developed a duplex probe-based real-time PCR (duplex qPCR) that facilitates the specific detection of chamois and a universal eukaryotic gene as control, especially in processed meat products. Detection of chamois was sensitive (chamois DNA content of 1.25 pg/mu L or 0.05% is detectable), robust (eight variations of PCR conditions tested), and specific (inclusivity and exclusivity). Detection of Eukarya in the duplex assay can be used both as an inhibition control, and as a rough estimation for the chamois content in the sample. We analysed 20 firm raw sausages with chamois as a declared ingredient. Eleven sausages were free of chamois meat, for three sausages, a Delta Cq((Chamois-Eukarya)) > 10 indicated the use of chamois meat as minor ingredient, and in six sausages, abundant amounts of chamois meat were detected. Furthermore, among three analysed chamois hams, one consisted of me deer meat instead. Conclusions: The developed duplex qPCR is an easy to use tool to verify the presence of the declared ingredient chamois meat, especially in processed meats like sausages. The considerable proportion of products lacking declared chamois meat (55% or 11/20 for firm raw sausages, 33% or 1/3 for ham), indicates the need for further controls in this non-standard food segment.

LebensmittelchemieVolume 74, Issue S1 p. S1-020-S1-020 Poster der 71. Arbeitstagung des Regionalverbands Bayern (9.–10. März 2020, Würzburg) Etablierung eines DNA-Barcoding-Verfahrens zur schnellen Identifizierung von Insekten Melanie Pavlovic, Melanie Pavlovic Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorMarzena Maggipinto, Marzena Maggipinto Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorMaximilian Krückel, Maximilian Krückel Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorJulia Pauly, Julia Pauly Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorTina Widmann, Tina Widmann Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorLars Gerdes, Lars Gerdes Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorIngrid Huber, Ingrid Huber Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this author Melanie Pavlovic, Melanie Pavlovic Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorMarzena Maggipinto, Marzena Maggipinto Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorMaximilian Krückel, Maximilian Krückel Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorJulia Pauly, Julia Pauly Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorTina Widmann, Tina Widmann Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorLars Gerdes, Lars Gerdes Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this authorIngrid Huber, Ingrid Huber Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit, Veterinärstraße 2, 85764 OberschleißheimSearch for more papers by this author First published: 05 May 2020 https://doi.org/10.1002/lemi.202051020AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume74, IssueS1Supplement: Vorträge und Poster der 71. Arbeitstagung des Regionalverbands BayernMarch 2020Pages S1-020-S1-020 RelatedInformation

Digital PCR (dPCR), as a new technology in the field of genetically modified (GM) organism (GMO) testing, enables determination of absolute target copy numbers. The purpose of our study was to test the transferability of methods designed for quantitative PCR (qPCR) to dPCR and to carry out an inter-laboratory comparison of the performance of two different dPCR platforms when determining the absolute GM copy numbers and GM copy number ratio in reference materials certified for GM content in mass fraction. Overall results in terms of measured GM% were within acceptable variation limits for both tested dPCR systems. However, the determined absolute copy numbers for individual genes or events showed higher variability between laboratories in one third of the cases, most possibly due to variability in the technical work, droplet size variability, and analysis of the raw data. GMO quantification with dPCR and qPCR was comparable. As methods originally designed for qPCR performed well in dPCR systems, already validated qPCR assays can most generally be used for dPCR technology with the purpose of GMO detection.

In 2015, the Bavarian Health and Food Safety Authority (Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit) initiated controls of Asian buffets to verify declaration of animal species. Especially Mongolian buffets, where raw meat of partly exotic animal species is offered with side dishes and sauces, enjoy high popularity in Germany. A total of 27 samples were collected in 5 Bavarian cities including nine mammalian meat, 1 frog, 3 crocodile, 10 fish, 1 squid, 2 shrimp, and 1 mussel sample. All samples were analyzed using molecular biological methods. The animal species was identified by DNA sequencing of the mitochondrial genes cytochrome c oxidase subunit I, cytochrome b or 16S ribosomal DNA with subsequent database mining. From the 27 samples, 5 were objectionable with either wrong or incomplete labelling. These included two fish samples, two samples falsely declared as zebra which were in fact beef, one guanaco sample which was depicted as camel and another guanaco sample which was marketed as llama. The results clearly show the need for continued surveillance of meat species in buffets covering a wide variety of meats and seafood.

The recent horse meat scandal that rocked Europe in early 2013 shows how important it is for the routine food control authorities to constantly evolve analytical tools for the accurate evaluation of meat products among others, to ensure that product declaration and actual composition correlate. While in most cases qualitative detection approaches suffice for product evaluation, in other cases a quantitative analysis is important to distinguish between inadvertent contamination and deliberate adulteration.In this work an optimized real-time qPCR-based approach is presented and compared with another real-time based method for the detection of equine sequences among others in meat products. The method is a multiplex system for the simultaneous quantification of horse, beef, pork and sheep fractions, and was validated for use in the routine analysis of meat products. We describe a modular multiplex approach, where a quadruplex system (without sheep) and a pentaplex assay (with an integrated sheep detection system) can be applied in meat detection and quantification strategies. The analysis is matrix independent and relies on concomitant quantification of the animal species present in the food sample against the backdrop of myostatin, a universal sequence present in most mammalian and poultry species. The limit of detection of the analytical method was 10 genome copy equivalents. For validation of the method, meat samples comprising differing meat compositions were analysed, and the assay performed well in terms of robustness and reproducibility. (C) 2016 Elsevier Ltd. All rights reserved.

Mit der klassischen Polymerase-Kettenreaktion (Polymerase Chain Reaction, PCR) werden bestimmte Zielabschnitte des Erbgutes (DNA) in einem einzigen Reaktionsansatz gezielt vervielfältigt und nachgewiesen. Bei der sogenannten droplet digital PCR (ddPCR) wird der Reaktionsansatz auf zehntausende winzige Tröpfchen (kleiner als 1 nL) verteilt (Wasser-in-Öl-Emulsion). Man erhält Tröpfchen, die entweder keine oder mindestens eine Kopie der Ziel-DNA enthalten. Anschließend wird eine PCR für jedes Tröpfchen durchgeführt, sodass sich entweder ein positives (1) oder ein negatives (0) Signal für jedes Tröpfchen ergibt. Diese 0/1-Antworten führten zu der Bezeichnung „digitale PCR“. Die ddPCR ermöglicht eine absolute Quantifizierung von DNA-Molekülen ohne Verwendung von Standardkurven.

Droplet digital polymerase chain reaction (ddPCR) has seen increasing applications in recent times, also in the analysis of genetically modified (GM) food and feed samples. While quantitative real-time PCR (qPCR) methods have been traditional mainstays till now, the applicability of ddPCR in routine analysis of GM food and feed has not yet been widely demonstrated. In this work, we applied ddPCR on selected GM-food and feed samples that were recently analyzed on the qPCR platform in inter-laboratory proficiency tests and showed good performance of the ddPCR method. Sometimes GM DNA at different transgene levels, useful as reference material is not readily available. Applying ddPCR, different concentrations of GM material, specifically transgene DNA at different levels (0.1–10%) useful as reference DNA, were generated from 100% non GM material and 100% transgene plant material respectively, and key performance parameters of the ddPCR assay evaluated. DdPCR performed well, indicating its suitability for the production of reference GM materials. In an expanded analysis, DNA extracted from a 100% GM reference soy plant (CV127) was appropriately diluted to low copy numbers and the absolute LOD95% determined at 2 copies (nominal value), comparing well with various published qPCR assays. In our inhibition studies, ddPCR showed a clear advantage over qPCR in SDS-inhibited samples, while its tolerance to other tested inhibitors was comparable with qPCR. Significantly, the qPCR assays demonstrated more asymmetrical amplification/inhibition with EDTA as inhibitor, with unequal inhibition in reference and transgene reactions, while inhibition was more symmetrical on the ddPCR platform. Finally, a panel of positive reference material with varying GM content from 0.1 to 10% were evaluated on the ddPCR platform and pertinent performance parameters assessed, namely, precision, accuracy and trueness of results, with good performance of the assay.

Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Background According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called ‘minimum required performance limit’ (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. Results We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section ‘Availability of supporting data’ for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. Conclusions The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in —but not limited to— the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations seem to be a reasonable minimum number to narrow down the possible spread of results.

Real-time PCR used to be the gold standard when it comes to detection of rare mutations, copy number variations or genetically modified organisms. A new option for DNA analyses is the digital PCR. In digital PCR, the reaction mix is distributed to many partitions and endpoint PCR is performed. The fraction of positive partitions can be used to calculate the initial concentration. Digital PCR is highly sensitive and allows quantification of absolute copy numbers without using a standard curve.