The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400–1000 nm) and a handheld NIR (900–1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95–100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.

Phylogenetic Groups and Antimicrobial Resistance Genes in Escherichia coli from Different Meat Species

Escherichia coli isolated from meat of different animal species may harbour antimicrobial resistance genes and may thus be a threat to human health. The objectives of this study were to define antimicrobial resistance genes in E. coli isolates from pork, beef, chicken- and turkey meat and analyse whether their resistance genotypes associated with phylogenetic groups or meat species. A total number of 313 E. coli samples were isolated using standard cultural techniques. In 98% of resistant isolates, a dedicated resistance gene could be identified by PCR. Resistance genes detected were tet(A) and tet(B) for tetracycline resistance, strA and aadA1 for streptomycin resistance, sulI and sulII for resistance against sulphonamides, dfr and aphA for kanamycin resistance and blaTEM for ampicillin resistance. One stx1 harbouring E. coli isolated from pork harboured the tet(A) gene and belonged to phylogenetic group B2, whilst another stx1 positive isolate from beef was multi-resistant and tested positive for blaTEM,aphA, strA–B, sulII, and tet(A) and belonged to phylogenetic group A. In conclusion, the distribution of resistance elements was almost identical and statistically indifferent in isolates of different meat species. Phylogenetic groups did not associate with the distribution of resistance genes and a rather low number of diverse resistance genes were detected. Most E. coli populations with different resistance genes against one drug often revealed statistically significant different MIC values.

Polymerase chain reaction in meat species identification

Lebensmittel gelten als authentisch, wenn das Produkt oder sein Inhalt mit dem Originalzustand und den Angaben auf der Deklaration übereinstimmt. Abweichungen von den Behauptungen und Angaben auf der Deklaration werden als Verstöße gegen die Lebensmittelvorschriften angesehen. Aufgrund des gestiegenen Bewusstseins für das Problem und die negativen Auswirkungen von Lebensmittelbetrug wird der Fleischkonsum maßgeblich von der Wahrnehmung der Verbraucher in Bezug auf die Lebensmittelqualität und Sicherheit beeinflusst. Daher ist eine genaue Etikettierung einer der wichtigsten Faktoren, die die Verbraucherpräferenzen bei der Auswahl und dem Kauf von Fleisch und Fleischerzeugnissen beeinflussen. Aus diesem Grund sind Analysemethoden zur Überprüfung der Echtheit von Fleisch und Fleischerzeugnissen wichtig, um die Produktqualität, Lebensmittelsicherheit und den Verbraucherschutz zu gewährleisten. Die Substitution von Fleischsorten ist nicht das einzige Kriterium für die Bestimmung der Echtheit von Fleisch und Fleischerzeugnissen, sondern auch die Herkunft des Fleisches, die Behandlung des Fleisches und der Zusatz von sonstigen Zutaten. Die Polymerase-Kettenreaktion (PCR) und die davon abgeleiteten Technologien haben sich als die am besten geeigneten Methoden zur Identifizierung von Arten in rohem und technologisch verarbeitetem Fleisch erwiesen. Die PCR-Methoden basieren hauptsächlich auf der Identifizierung der Zielregion der mitochondrialen DNA, was den Nachweis von Arten in einer breiten Palette von Fleischerzeugnissen ermöglicht, einschließlich aller Haustiere und des für den menschlichen Verzehr bestimmten Wildfleischs. Sie haben jedoch auch einige Nachteile. So eignet sich beispielsweise die zufällig amplifizierte polymorphe DNA (PCR-RAPD) nicht für den Artennachweis in Fleischmischungen und in thermisch verarbeitetem Fleisch. Andererseits sind einige Methoden teuer und zeitaufwendig, und die Ergebnisse sind schwer zu interpretieren. In diesem Artikel werden die wichtigsten PCR-basierten Methoden zur Identifizierung von Fleischarten vorgestellt und beschrieben.

Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm−1 and 2800–3000 cm−1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

Electronic Tongue as a Correlative Technique for Modeling Cattle Meat Quality and Classification of Breeds

Discrimination and species identification of meat has always been of paramount importance in the European meat market. This is often achieved using different conventional analytical methods but advanced sensor-based methods, such as the electronic tongue (e-tongue), are also gaining attention for rapid and reliable analysis. The aim of this study was to discriminate Angus, domestic buffalo, Hungarian Grey, Hungarian Spotted cattle, and Holstein beef meat samples from the chuck steak part of the animals, which mostly contained longissimus dorsi muscles, using e-tongue as a correlative technique with conventional methods for analysis of pH, color, texture, water activity, water-holding capacity, cooking yield, water binding activity, and descriptive sensory analysis. Analysis of variance (ANOVA) was used to determine significant differences between the measured quality traits of the five-meat species after analysis with conventional analytical methods. E-tongue data were visualized with principal component analysis (PCA) before classifying the five-meat species with linear discriminant analysis (LDA). Significant differences were observed among some of the investigated quality parameter. In most cases, Hungarian Grey was most different from the other species. Using e-tongue, separation patterns could be observed in the PCA that were confirmed with 100% recognition and 97.5% prediction of all the different meat species in LDA.

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Real-time PCR High-resolution Melting Analysis for the Species Identification of Meat Products: Focusing on Food Safety and Detection of Meat Adulterations

Background: Real-time polymerase chain reaction (PCR) and high-resolution melting (HRM) analysis are currently considered as reliable techniques for the species identification of meat-based products and widely used to detect meat adulteration. Objectives: To examine the validity of real-time PCR and HRM analysis to identify meat species in meat-based products. Methods: Meat samples from five species (i.e., cattle, sheep, chicken, turkey, and wild pig) were purchased. Minced meat from the animal species of interest was prepared at the purities of 10%, and 20% and also were prepared as single and mixtures of two species. For molecular assessments, DNA samples were extracted from all the meat samples and subjected to real-time PCR by amplifying a mitochondrial cytochrome b specific for each species. Results: All the meat species studied in this research were successfully detected in the mixed meat samples when separately examined by real-time PCR. High-resolution melting analysis showed that all the meat species of interest were efficiently distinguished when examined simultaneously. Conclusions: The data presented here shows that the real-time PCR and HRM analysis are reliable methods for the identification of meat species used in meat products.