Analytical methods used for the authentication of food of animal origin

Etablierung der § 64 LFGB Arbeitsgruppen „NGS – Bakteriencharakterisierung“ und „NGS – Speziesidentifizierung“

Das Next-Generation Sequencing (NGS) besitzt großes Potential im Bereich der Lebensmittelsicherheit und der Authentizitätsprüfung von Lebensmitteln. Die Gesamtgenomsequenzierung mikrobieller Genome kombiniert mit bioinformatischen Auswerteprogrammen ersetzt zunehmend die klassischen Typisierungsmethoden und gilt aufgrund ihres außerordentlichen Auflösungsvermögen mittlerweile als Methode der Wahl im Rahmen von Ausbruchsuntersuchungen. Zur Authentizitätskontrolle z. B. von Fleisch- und Fischproben finden NGS-Methoden als Metabarcoding immer häufiger Anwendung, um Täuschung und Irreführung bis hin zu Lebensmittelbetrug aufzudecken. Einige Untersuchungsbehörden verfügen bereits über die NGS-Technologie und setzen diese auch erfolgreich ein, weitere Einrichtungen werden folgen. Um den mit der Lebensmittelüberwachung betrauten Behörden validierte, leistungsfähige und standardisierte NGS-Methoden zur Verfügung zu stellen, ist eine Aufnahme dieser Methoden in die „Amtliche Sammlung von Verfahren zur Probenahme und Untersuchung von Lebensmitteln“ (ASU) durch die Gründung zweier neuer § 64 LFGB Arbeitsgruppen mit unterschiedlichen thematischen Schwerpunkten vorgesehen. Die Arbeitsgruppe „NGS – Bakteriencharakterisierung“ bearbeitet NGS-Verfahren für die Sequenzierung bakterieller Erreger im Rahmen von Ausbruchsuntersuchungen. Die Arbeitsgruppe „NGS – Speziesidentifizierung“ beschäftigt sich mit NGS-Methoden zur Tierartendifferenzierung in Lebensmitteln. Am 6. März 2019 fand das erste Treffen der Arbeitsgruppe „NGS – Speziesidentifizierung“ und am folgenden Tag, dem 7. März 2019 das der Arbeitsgruppe „NGS – Bakteriencharakterisierung“ auf Einladung des Bundesamts für Verbraucherschutz und Lebensmittelsicherheit (BVL) in Berlin statt. Auf den Sitzungen wurden durch die Mitglieder der Gruppen NGS-Methoden zur Bakteriencharakterisierung bzw. zur Tierartendifferenzierung in Lebensmitteln vorgestellt. Anschließend diskutierten die Mitglieder die ersten thematischen Schwerpunkte der Methodenentwicklung, Validierungskonzepte, Qualitätskontrollmaßnahmen und den Einsatz dieser Methoden in der Lebensmittelüberwachung. Es wurde beschlossen, durch laborübergreifende Vorringversuche die Vergleichbarkeit der verschiedenen NGS-Technologien zu ermitteln sowie die entsprechenden Auswerteparameter, Qualitätskriterien und Validierungsparameter für eine laborübergreifende Validierungsstudie zu erarbeiten.

Verification of cheeses authenticity by mass spectrometry

Cheeses are a group of fermented dairy products that are produced all over the world in various forms and flavours. Milk, especially sheep or goat milk, is still regarded as an expensive raw material in the world, which makes milk and milk products highly attractive as a fraud target. Most often, such fraud includes partial or complete substitution with cheaper sorts of milk (e.g. bovine milk). The aim of this work was to verify the authenticity of 27 cheeses commonly emerging on the Czech food market. The cheeses were distinguished on the basis of milk animal species origin. For this purpose, two mass spectrometry techniques were used: matrix-assisted laser desorption/ionization with time of flight mass spectrometry together with principal component analysis method and liquid chromatography coupled with electrospray ionization and quadrupole time-of-flight mass spectrometry. The results were a partial success, because the cheeses could only be partially distinguished with the first mass spectrometry technique probably because of the influence of some protein additive materials in cheeses. The second technique allowed for collecting higher quality results and thus appears to be highly suitable for the research task.

The employment of q-PCR using specific primer targeting on mitochondrial cytochrome-b gene for identification of wild boar meat in meatball samples

Objective:

The objective of this study was to employ real-time or quantitative polymerase chain reaction (q-PCR) using novel species specific primer (SSP) targeting on mitochondrial cytochrome-b of wild boar species (CYTBWB2-wb) gene for the identification of non-halal meat of wild boar meat (WBM) in meatball products.

Materials and Methods:

The novel SSP of CYTBWB2-wb was designed by our group using PRIMERQUEST and NCBI software. DNA was extracted using propanol-chloroform-isoamyl alcohol method. The designed SSP was further subjected for validation protocols using DNA isolated from fresh meat and from meatball, which include specificity test, determination of efficiency, limit of detection and repeatability, and application of developed method for analysis of commercially meatball samples

Results:

The results showed that CYTBWB2-wb was specific to wild boar species against other animal species with optimized annealing temperature of 59°C. The efficiency of q-PCR obtained was 91.9% which is acceptable according to the Codex Allimentarius Commission (2010). DNA, with as low as 5 pg/μl, could be detected using q-PCR with primer of CYTBWB2-wb. The developed method was also used for DNA analysis extracted from meatball samples commercially available.

Conclusion:

q-PCR using CYTBWB2-wb primers targeting on mitochondrial cytochrome-b gene (forward: CGG TTC CCT CTT AGG CAT TT; Reverse: GGA TGA ACA GGC AGA TGA AGA) can be fruitfully used for the analysis of WBM in commercial meatball samples.

Carbon-Based Nanomaterials in Sensors for Food Safety

Food safety is one of the most important and widespread research topics worldwide. The development of relevant analytical methods or devices for detection of unsafe factors in foods is necessary to ensure food safety and an important aspect of the studies of food safety. In recent years, developing high-performance sensors used for food safety analysis has made remarkable progress. The combination of carbon-based nanomaterials with excellent properties is a specific type of sensor for enhancing the signal conversion and thus improving detection accuracy and sensitivity, thus reaching unprecedented levels and having good application potential. This review describes the roles and contributions of typical carbon-based nanomaterials, such as mesoporous carbon, single- or multi-walled carbon nanotubes, graphene and carbon quantum dots, in the construction and performance improvement of various chemo- and biosensors for various signals. Additionally, this review focuses on the progress of applications of this type of sensor in food safety inspection, especially for the analysis and detection of all types of toxic and harmful substances in foods.

Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions

Milk samples from 1264 cows in 85 farms were authenticated for different farming-systems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared spectra (FTIRS) and gas-chromatographic fatty-acid (FA) profiles. FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improved the results. All milk samples were processed into ripened model-cheeses, and analyzed by near-infrared-spectrometry (NIRS), by proton-transfer-reaction time-of-flight mass-spectrometry for their volatile organic compound (VOCs) fingerprint and by panel sensory profiling (SENS). Farming-system authentication on cheese samples was less efficient than on milk, but still possible. The instrumental methods yielded similar validation results, better than SENS, and their combination improved the correct classification rate. The efficiency of the different technics was affected by specific farming systems. In conclusion, dairy products could be discriminated for farming-systems with acceptable accuracy, but the methods tested differ in sampling procedure, rapidity and costs.

Combining near-infrared hyperspectral imaging with elemental and isotopic analysis to discriminate farm-raised pacific white shrimp from high-salinity and low-salinity environments

White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall accuracy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and ¹³C concentration. This agrees with the fact that there is more ¹³C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments.

Approaching Authenticity Issues in Fish and Seafood Products by Qualitative Spectroscopy and Chemometrics

The intrinsically complex nature of fish and seafood, as well as the complicated organisation of the international fish supply and market, make struggle against counterfeiting and falsification of fish and seafood products very difficult. The development of fast and reliable omics strategies based on spectroscopy in conjunction with multivariate data analysis has been attracting great interest from food scientists, so that the studies linked to fish and seafood authenticity have increased considerably in recent years. The present work has been designed to review the most promising studies dealing with the use of qualitative spectroscopy and chemometrics for the resolution of the key authenticity issues of fish and seafood products, with a focus on species substitution, geographical origin falsification, production method or farming system misrepresentation, and fresh for frozen/thawed product substitution. Within this framework, the potential of fluorescence, vibrational, nuclear magnetic resonance, and hyperspectral imaging spectroscopies, combined with both unsupervised and supervised chemometric techniques, has been highlighted, each time pointing out the trends in using one or another analytical approach and the performances achieved.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

Duplex PCR-ELONA for the detection of pork adulteration in meat products

In this work, a duplex PCR-Enzyme Linked Oligonucleotide Assay (ELONA) is reported for the sensitive and reliable detection of pork adulteration in beef and chicken products, two of the most widely consumed meat types in the world. The strategy relies on the use of species-specific tailed primers for duplex amplification and simple dilution of the PCR reactions for direct colorimetric detection via hybridization, eliminating the need for any other post-amplification steps. A high sensitivity was achieved, with as low as 71-188 pg of genomic DNA able to be detected using mixtures of control DNA from each species. The strategy was validated using DNA add-mixtures as well as DNA extracted from raw meat mixtures and 0.5-1% w/w pork could be easily detected when mixed with beef or chicken. The proposed approach is simple, sensitive and cost-effective compared to equivalent commercial kits suitable for detecting adulterant pork levels in meat products.

Absolute quantification of targeted meat and allergenic protein additive peptide markers in meat products

We present an implementation of the absolute quantification (AQUA) method for monitoring of peptide abundance in complex mixtures of processed proteins. Specific peptide markers from meats (chicken, duck, goose, pork and beef) and common protein allergenic additives (soy, milk and egg white preparations) were chosen and synthesised with stable isotopes (13C and 15N) for use as internal standards. A wide range of food samples, from cooked or raw meat to sterilised pâté, was analysed by a triggered multiple reaction monitoring mode experiment and triple quadrupole mass spectrometry for the direct measure of tryptic peptides representing the amounts of specific proteins. Considerable differences among the abundances of meat and non-meat proteins were observed, and illegal addition and replacement of ingredients were discovered, i.e. undeclared addition of pork and egg white proteins, and illegal substitution of veal, goose and duck meat with cheaper pork.

Rapid authentication of European sea bass (Dicentrarchus labrax L.) according to production method, farming system, and geographical origin by near infrared spectroscopy coupled with chemometrics

Chemometric analysis of near-infrared spectroscopy (NIRS) data was applied to investigate the possibility to rapidly authenticate European sea bass (Dicentrarchus labrax L.) according to production method (wild or farmed), rearing system (extensive, semi-intensive or intensive), and geographical origin (Western, Central or Eastern Mediterranean Sea). NIR spectra from 1100 to 2500 nm were subjected to an exploratory principal component analysis (PCA) followed by orthogonal partial last square-discriminant analysis (OPLS-DA) to develop classifiers able to distinguish samples according to the various conditions under study. Models provided a correct classification rate of 100% for both wild and farmed sea bass, and of 67%, 80%, 100% for extensively, semi-intensively, and intensively-reared subjects, respectively. As for geographical provenance, 100% of Eastern, 88% of Central and 85% of Western Mediterranean Sea samples were correctly discriminated. The successful results obtained confirmed suitability of chemometric analysis applied to NIRS data for fast authentication of European sea bass origin.

Tetraplex real-time PCR assay for the simultaneous identification and quantification of roe deer, red deer, fallow deer and sika deer for deer meat authentication

Analytical methods are needed for the identification and quantification of meat species to detect food adulteration. Since game meat is more expensive than meat from domesticated animal species, it is a potential target for adulteration. We present a tetraplex real-time PCR assay that allows the simultaneous determination of the content of roe deer, red deer, fallow deer and sika deer. The tetraplex assay showed only moderate cross-reactivity with closely related species. After optimization the tetraplex assay had a limit of detection of 0.1% (w/w) and a limit of quantification of 0.5% (w/w) for each of the four deer species. The tetraplex assay was found to be robust, slight modifications of the experimental setup did not lower its performance. Recoveries obtained by analyzing DNA mixtures and DNA isolates from model game sausages were similar to those obtained with the singleplex assays.

Carbon Stable-Isotope and Physicochemical Data as a Possible Tool to Differentiate between Honey-Production Environments in Uruguay

The allocation of honey origin is an increasingly important issue worldwide as it is closely related to product quality and consumer preference. In South America, honeys produced in grasslands and eucalyptus or native forests are preferred at the regional level, so their differentiation is essential to assure consumers of their authenticity according to their productive system. The objective of this study was to differentiate honeys produced in three environments: one, a monoculture system based on the eucalyptus forest, and two others based in natural environments of grasslands and native forests. To do this, honey’s physicochemical and isotopic variables (pH, free acidity, lactic acid content, moisture, total sugar content, and honey and extracted protein 13C isotopic composition) were analysed. Discriminant analysis applied to the data revealed that, based on the selected variables, it was impossible to differentiate the three groups of honeys due to the superposition of those produced in grasslands and native forests. For this reason, a group of honeys derived from native and polyfloral environments (grasslands and native forests) was formed and subjected to discriminant analysis (DA), together with the group of honeys derived from a commercial plantation of eucalyptus forest. The model obtained in this case achieved 100% correct allocation both at the training stage and the cross-validation stage.