Genetic traceability of livestock products: A review

Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high-throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios. Our results showed that, when using a training set of 16 individuals per breed and 32 features (SNPs), the accuracy of breed identification with feature selection (eXtreme Gradient Boosting, XGBoost) could exceed 95.00% (nine breeds), and was improved by 7.04% over the results with random selection. For stacking ensemble learning, feature selection methods (including random selection method) were used before different base learners. When these base learners' training set had 16 individuals per breed and 32 features, the accuracy of stacking ensemble learning improved by 9.24% over the best base learner (nine breeds), but did not significantly increase the advantage over the models with XGBoost feature selection. When using a training set of 16 individuals and 512 features per breed, breed identification with anomaly detection (local outlier factor, LOF) and random selection could achieve an accuracy of 89.06% (15 breeds). These results show that machine learning could be an effective tool for breed identification and this study will also provide useful information for the application of machine learning in animal genetics and breeding.

Meat Traceability: Traditional Market Shoppers' Preferences and Willingness-to-Pay for Additional Information in Taiwan

Due to food scandals that shocked the retailer markets, traceability systems were advocated to regain consumers' confidence and trust. However, while traceability systems can be more easily explored in modern markets, almost no traceability system can be found in traditional markets in Taiwan, especially when buying meat products. This study explored the preference and the willingness-to-pay (WTP) for traceability information of pork products in traditional markets in Taiwan. The random utility theory (RUT) with the contingent valuation method (CVM) was adopted to examine the total of 1420 valid responses in Taiwan. Results show that 80% of traditional market consumers are willing to pay more for traceability information of pork products. Specifically, when consumers (1) know the market price of pork, (2) do not often buy food in the traditional market, (3) live in south or north regions of Taiwan, (4) have a flexible buying schedule, (5) are aware of food safety due to frequently accessing health-related content through media, or (6) think pork grading is very important, they would tend to choose meat products with traceability information. The implication of this study suggests that there is an urgent desire for food safety labeling and traceability information system in traditional markets in Taiwan. Especially, those who usually shop in the higher-price markets are willing to pay the most for this traceability information.

Distributed Interoperable Records: The Key to Better Supply Chain Management

The global supply chain is a network of interconnected processes that create, use, and exchange records, but which were not designed to interact with one another. As such, the key to unlocking the full potential of supply chain management (SCM) technologies is achieving interoperability across participating records systems and networks. We review existing research and solutions using distributed ledger technology (DLT) and provide a survey of its current state of practice. We additionally propose a holistic solution: a DLT-based interoperable future state that could enable the interoperable, efficient, reliable, and secure exchange of records with integrity. Finally, we provide a gap analysis between our proposed future state and the current state, which also serves as a gap analysis for many fractional DLT-based SCM solutions and research.

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

Molecular Identification of the "Facciuta Della Valnerina" Local Goat Population Reared in the Umbria Region, Italy

Italy holds important genetic resources of small ruminant breeds. By distinguishing goat breeds at the DNA level, certification of products from specific breeds can be valorized. The aim of this study was to establish the genetic identity of Facciuta della Valnerina, a local goat population of Italy, compared with the cosmopolitan breeds, Saanen and Camosciata delle Alpi, reared in the same geographic area. A total of 116 microsatellite alleles ranging from 4 to 13 were detected at 16 loci in the three goat populations/breeds. A total of 23 private alleles with frequencies lower than 0.3 were detected in the Facciuta della Valnerina population. The mean numbers of alleles were 6.67, 4.58, and 4.92 in Facciuta della Valnerina, Camosciata delle Alpi, and Saanen, respectively. The expected heterozygosity ranged from 0.20 to 0.86. Most loci were highly polymorphic and informative (polymorphic information content ≥0.50). Factorial correspondence analysis and principal components analysis revealed very clear separation between Facciuta della Valnerina and the two reference goat breeds. Reducing the number of markers from 16 to 12 (on the basis of polymorphic information content and the number of alleles) still allowed us to distinguish the local population, indicating that microsatellite markers are capable of discriminating local livestock breeds at a low cost.

Development of a real-time PCR assay for the identification and quantification of bovine ingredient in processed meat products

In order to find fraudulent species substitution in meat products, a highly sensitive and rapid assay for meat species identification and quantification is urgently needed. In this study, species-specific primers and probes were designed from the mitochondrial cytb (cytochrome b) fragment for identification and quantification of bovine ingredient in commercial meat products. Bovine samples and non-bovine ones were used to identify the specificity, sensitivity, and applicability of established assay. Results showed that the primers and probes were highly specific for bovine ingredient in meat products. The absolute detection limit of the real-time PCR method was 0.025 ng DNA, and the relative detection limit was 0.002% (w/w) of positive samples. The quantitative real-time PCR assay was validated on simulated meat samples and high in the precision and accuracy. In order to demonstrate the applicability and reliability of the proposed assay in practical products, the 22 commercial meat products including salted, jerkies, and meatball were used. The results indicated the established method has a good stability in detection of bovine ingredient in real food. The established method in this study showed specificity and sensitivity in identification and quantification of beef meat in processed meat products.

Myostatin (MSTN) Gene Indel Variation and Its Associations with Body Traits in Shaanbei White Cashmere Goat

Myostatin (MSTN) gene, also known as growth differentiation factor 8 (GDF8), is a member of the transforming growth factor-beta super-family and plays a negative role in muscle development. It acts as key points during pre- and post-natal life of amniotes that ultimately determine the overall muscle mass of animals. There are several studies that concentrate on the effect of a 5 bp insertion/deletion (indel) within the 5' untranslated region (5' UTR) of goat MSTN gene in goats. However, almost all sample sizes were below 150 individuals. Only in Boer goats, the sample sizes reached 482. Hence, whether the 5 bp indel was still associated with the growth traits of goats in large sample sizes which were more reliable is not clear. To find an effective and dependable DNA marker for goat rearing, we first enlarged the sample sizes (n = 1074, Shaanbei White Cashmere goat) which would enhance the robustness of the analysis and did the association analyses between the 5 bp indel and growth traits. Results uncovered that the 5 bp indel was significantly related to body height, height at hip cross, and chest width index (p < 0.05). In addition, individuals with DD genotype had a superior growing performance than those with the ID genotype. These findings suggested that the 5 bp indel in MSTN gene are significantly associated with growth traits and the specific genotype might be promising for maker-assisted selection (MAS) of goats.

Analysis of genome-wide DNA arrays reveals the genomic population structure and diversity in autochthonous Greek goat breeds

Goats play an important role in the livestock sector in Greece. The national herd consists mainly of two indigenous breeds, the Eghoria and Skopelos. Here, we report the population structure and genomic profiles of these two native goat breeds using Illumina’s Goat SNP50 BeadChip. Moreover, we present a panel of candidate markers acquired using different genetic models for breed discrimination. Quality control on the initial dataset resulted in 48,841 SNPs kept for downstream analysis. Principal component and admixture analyses were applied to assess population structure. The rate of inbreeding within breed was evaluated based on the distribution of runs of homozygosity in the genome and respective coefficients, the genomic relationship matrix, the patterns of linkage disequilibrium, and the historic effective population size. Results showed that both breeds exhibit high levels of genetic diversity. Level of inbreeding between the two breeds estimated by the Wright’s fixation index F_ST was low (Fst = 0.04362), indicating the existence of a weak genetic differentiation between them. In addition, grouping of farms according to their geographical locations was observed. This study presents for the first time a genome-based analysis on the genetic structure of the two indigenous Greek goat breeds and identifies markers that can be potentially exploited in future selective breeding programs for traceability purposes, targeted genetic improvement schemes and conservation strategies.

Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images

Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.

Tracing individuals and populations of the tambaqui, Colossoma macropomum (Cuvier, 1818), from Brazilian hatcheries using microsatellite markers

BACKGROUND

In recent years, tracing of alimentary produce of animal origin has become increasingly important, for economic, food safety and ecological reasons. The tambaqui, Colossoma macropomum, is the native fish most farmed in Brazil. The reliable identification of the origin of tambaquis (wild or farmed) offered for sale to the general public has become necessary to satisfy regulatory norms and uphold consumer confidence. Molecular methods based on the analysis of DNA sequences have often been used to evaluate the potential for tracing farmed fish, given their reliability and precision.

RESULTS

Full likelihood and Bayesian approaches proved to be the most efficient for the identification, respectively, of individuals and populations for most of the fish sampled from seven hatcheries and one wild stock. The exclusion method and genetic distances were the least effective approaches for the identification of individuals and populations. The Bayesian method identified correctly more than 99% of the fry from most stocks, except those of the Santarém hatchery and River Amazon wild stock, which presented the best results for individual identification.

CONCLUSIONS

The identification of populations was effective for most hatcheries, although the identification of individuals from most stocks was hampered by the reduced genetic variability. © 2018 Society of Chemical Industry.

Genetic traceability practices in a large-size beef company in China

An effective and trustworthy traceability system is important for food safety and quality; however, traditional traceability systems that only rely on the recording method do not completely prevent food fraud. DNA-based traceability techniques facilitate seamless connectivity within the entire food supply chain. A convenient and low-cost ear tag device was invented for collecting animal blood samples as an identity control, and a panel including 12 single nucleotide polymorphic (SNP) loci was selected to distinguish individuals with a matching probability of 1.70 × 10^-5. The exact animal individual was identified by comparing the SNP genotype barcodes between the meat and blood samples derived from the recording system to further validate authenticity of the recording system. These results illustrate that a combination of the genetic traceability method and a traditional recording system can provide trustworthy traceability for consumers.

Distinguishing industrial meat from that of indigenous chickens with molecular markers

The aim of investigation was to evaluate a traceability system to detect industrial chicken meat among indigenous products, considering issues that could affect assignment accuracy. The dataset included 2 Italian indigenous meat breeds, namely Bionda Piemontese (2 ecotypes) and Bianca di Saluzzo, one broiler line, and 3 layer lines. Assignment tests were performed using a standard panel of 28 microsatellite loci. To evaluate effects of inbreeding and substructure on assignment accuracy, a simulated dataset was prepared. Broilers and layers belong to homogeneous populations and never enter the clusters of indigenous breeds. Ambiguity or misallocation are expected between the Bionda ecotypes and between the 2 indigenous breeds, but it is unlikely that niche products provided by Bionda and Bianca will compete with one another. Non-random mating reduces accuracy, but only populations having weak genetic differentiation are involved, namely those that are less interesting to discriminate. The dataset can be used as a reference population to distinguish commercial meat from indigenous meat with great accuracy. Misallocations increase as number of loci decreases, but only within or between the indigenous breeds. A subpanel of the most resolving 14 loci keeps sufficient informative content to provide accuracy and to correctly allocate additional test samples within the reference population. This analytical tool is economically sustainable as a method to detect fraud or mislabeling. Adoption of a monitoring system should increase the value of typical products because the additional burden of molecular analyses would improve commercial grade and perception of quality.

Selection and use of microsatellite markers for individual identification and meat traceability of six swine breeds in the Chinese market

Meat traceability based on molecular markers is exerting a great influence on food safety and will enhance its key role in the future. This study aimed to investigate and verify the polymorphism of 23 microsatellite markers and select the most suitable markers for individual identification and meat traceability of six swine breeds in the Chinese market. The mean polymorphism information content value of these 23 loci was 0.7851, and each locus exhibited high polymorphism in the pooled population. There were 10 loci showing good polymorphism in each breed, namely, Sw632, S0155, Sw2406, Sw830, Sw2525, Sw72, Sw2448, Sw911, Sw122 and CGA. When six highly polymorphic loci were combined, the match probability value for two random individual genotypes among the pig breeds (Beijing Black, Sanyuan and Taihu) was lower than 1.151 E-06. An increasing number of loci indicated a gradually decreasing match probability value and therefore enhanced traceability accuracy. The validation results of tracing 18 blood and corresponding meat samples based on five highly polymorphic loci (Sw2525, S0005, Sw0107, Sw911 and Sw857) were successful, with 100% conformation probability, which provided a foundation for establishing a traceability system for pork in the Chinese market.

Pig identification and meat traceability by multiallelic amplification fragments with multiple single nucleotide polymorphisms

Compared with conventional identification methods, DNA-based genetic approaches such as single nucleotide polymorphisms (SNPs) and satellites are much more reliable for pig identification and meat traceability. In this study, multiallelic amplification fragments with multiple SNPs, incorporating the advantages of both SNPs and microsatellites, were explored for the first time for pig identification and meat traceability. Primer pairs for multiallelic fragments and their optimal SNPs were successfully selected and used for identification of individuals from Suzhong and Duroc populations. Meanwhile, the combined panel of the above mentioned primer pairs together with their optimal SNPs for Suzhong and/or Duroc pigs were validated for identification of the hybrids (Suzhong×Duroc). Therefore, we have successfully selected multiallelic amplification fragments with multiple SNPs to identify pigs and their meat samples from Suzhong, Duroc or their hybrids. Our study demonstrates that our method is more powerful for pig identification or meat traceability than SNPs or microsatellites.

Tracing the breeding farm of domesticated pig using feature selection (Sus scrofa)

Objective

Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection.

Methods

We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers.

Results

We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs.

Conclusion

The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

Associations of the Polymorphisms in DHRS4, SERPING1, and APOR Genes with Postmortem pH in Berkshire Pigs

Postmortem pH is a main factor influencing the meat quality in pigs. This study investigated the association of postmortem pH with single-nucleotide polymorphisms (SNPs) in the fourth member of the short-chain dehydrogenase/reductase family (DHRS4), the first member of serpin peptidase inhibitor, clade G (complement inhibitor) (SERPING1), and the apolipoprotein R precursor (APOR) genes in Berkshire pigs. The study included 437 pigs, and genotyping was conducted using the GoldenGate Assay (Illumina, San Diego, CA, USA). DHRS4, SERPING1, and APOR polymorphisms were significantly associated with pH₄₅ or pH₂₄ (p < 0.05). SERPING1 was also statistically significantly associated with water holding capacity (p < 0.05), which is closely associated with postmortem pH. These results suggest that SNPs in the DHRS4, SERPING1, and APOR genes have potential for use as genetic markers for the meat quality in pigs.

STR Profiling for Discrimination between Wild and Domestic Swine Specimens and between Main Breeds of Domestic Pigs Reared in Belarus

A panel comprising 16 short tandem repeats (STRs) and a gender-specific amelogenin marker was worked out and tested for robustness in discrimination between wild and domestic swine subspecies encountered in Europe, between regional populations of wild boars and between main breeds of domestic pigs reared in Belarus. The STR dataset comprised 310 wild boars, inhabiting all administrative regions of Belarus, and 313 domestic pigs, representing three local and three cosmopolitan lines. Additionally, a total of 835 wild boars were genotyped for the presence of melanocortin 1 receptor (MC1R) alleles specific for domestic pigs. Correctness of assignment of STR profiles to appropriate populations was measured by log-likelihood ratios (log-LRs). All samples were correctly identified as wild boars or domestic pigs with average log-LR of 42.4 (LR = 2.6×10¹⁸). On the other hand, as many as 50 out of 835 (6.0%) genotyped wild boars from Belarus possessed MC1R alleles specific to domestic pigs, demonstrating supremacy of our STR profiling system over traditional differentiation between wild boars and domestic pigs, based on single binary markers. Mean log-LRs for allocation of wild boars to their regions of origin and of domestic pigs to appropriate breeds were 2.3 (LR = 9.7) and 13.4 (LR = 6.6×10⁵), respectively. Our results demonstrate the developed STR profiling system to be a highly efficient tool for differentiation between wild and domestic swine subspecies and between diverse breeds of domestic pigs as well as for verification of genetic identity of porcine specimens for the purpose of forensic investigations of wildlife crimes, assurance of veterinary public health, parentage control in animal husbandry, food safety management and traceability of livestock products.

Relationships between Single Nucleotide Polymorphism Markers and Meat Quality Traits of Duroc Breeding Stocks in Korea

This study was conducted to determine the relationships of five intragenic single nucleotide polymorphism (SNP) markers (protein kinase adenosine monophosphate-activated γ3 subunit [PRKAG3], fatty acid synthase [FASN], calpastatin [CAST], high mobility group AT-hook 1 [HMGA1], and melanocortin-4 receptor [MC4R]) and meat quality traits of Duroc breeding stocks in Korea. A total of 200 purebred Duroc gilts from 8 sires and 40 dams at 4 pig breeding farms from 2010 to 2011 reaching market weight (110 kg) were slaughtered and their carcasses were chilled overnight. Longissimus dorsi muscles were removed from the carcass after 24 h of slaughter and used to determine pork properties including carcass weight, backfat thickness, moisture, intramuscular fat, pH24h, shear force, redness, texture, and fatty acid composition. The PRKAG3, FASN, CAST, and MC4R gene SNPs were significantly associated with the meat quality traits (p<0.003). The meats of PRKAG3 (A 0.024/G 0.976) AA genotype had higher pH, redness and texture than those from PRKAG3 GG genotype. Meats of FASN (C 0.301/A 0.699) AA genotype had higher backfat thickness, texture, stearic acid, oleic acid and polyunsaturated fatty acid than FASN CC genotype. While the carcasses of CAST (A 0.373/G 0.627) AA genotype had thicker backfat, and lower shear force, palmitoleic acid and oleic acid content, they had higher stearic acid content than those from the CAST GG genotype. The MC4R (G 0.208/A 0.792) AA genotype were involved in increasing backfat thickness, carcass weight, moisture and saturated fatty acid content, and decreasing unsaturated fatty acid content in Duroc meat. These results indicated that the five SNP markers tested can be a help to select Duroc breed to improve carcass and meat quality properties in crossbred pigs.

Genetic Diversity Analysis of South and East Asian Duck Populations Using Highly Polymorphic Microsatellite Markers

Native duck populations have lower productivity, and have not been developed as much as commercials duck breeds. However, native ducks have more importance in terms of genetic diversity and potentially valuable economic traits. For this reason, population discriminable genetic markers are needed for conservation and development of native ducks. In this study, 24 highly polymorphic microsatellite (MS) markers were investigated using commercial ducks and native East and South Asian ducks. The average polymorphic information content (PIC) value for all MS markers was 0.584, indicating high discrimination power. All populations were discriminated using 14 highly polymorphic MS markers by genetic distance and phylogenetic analysis. The results indicated that there were close genetic relationships among populations. In the structure analysis, East Asian ducks shared more haplotypes with commercial ducks than South Asian ducks, and they had more independent haplotypes than others did. These results will provide useful information for genetic diversity studies in ducks and for the development of duck traceability systems in the market.

Non-invasive sensing for food reassurance

The main food quality traits of interest using non-invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health-protecting properties, nutritional characteristics and safety. A wide range of non-invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, X-ray, biosensor, microwave and terahertz, are organized according to physical principle.

Application of LogitBoost Classifier for Traceability Using SNP Chip Data

Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.

Effectiveness of a 95 SNP panel for the screening of breed label fraud in the Chinese meat market

Breed assignment has proved to be useful to control meat trade and protect the value of special productions. Meat-related frauds have been detected in China; therefore, 95 SNPs selected from the ISAG core panel were evaluated to develop an automated and technologically updated tool to screen breed label fraud in the Chinese meat market. A total of 271 animals from four Chinese yellow cattle (CYC) populations, six Bos taurus breeds, two Bos indicus and one composite were used. The allocation test distinguished European, Japanese and Zebu breeds, and two Chinese genetic components. It correctly allocated Japanese Black, Zebu and British breeds in 100, 90 and 89% of samples, respectively. CYC evidenced the Zebu, Holstein and Limousin introgression. The test did not detect CYC components in any of the 25 samples from Argentinean butchers. The method could be useful to certify Angus, Hereford and Japanese Black meat, but a modification in the panel would be needed to differentiate other breeds.

The perceived value of dairy product traceability in modern society: An exploratory study

The current study assessed the perceived value of food traceability in modern society by young consumers. After experiencing numerous recalls and food safety-related incidences, consumers are increasingly aware of the tools available to mitigate risks. Food traceability has been associated with food safety procedures for many years, but recent high-profile cases of food fraud around the world have given traceability a different strategic purpose. Focusing solely on dairy products, our survey results offer a glimpse of consumer perceptions of traceability as a means to preserve food integrity and authenticity. This study explored the various influences that market-oriented traceability has had on dairy consumers. For example, results show that if the dairy sector could guarantee that their product is in fact organic, 53.8% of respondents who often purchase organic milk would consider always purchasing traceable organic milk. This research produced a quantitative set of information related to the perceived value of food traceability, which could be useful for the creation and development of improved guidelines and better education for consumers. We discuss limitations and suggest areas for new research.

Labeling milk along its production chain with DNA encapsulated in silica

The capability of tracing a food product along its production chain is important to ensure food safety and product authenticity. For this purpose and as an application example, recently developed Silica Particles with Encapsulated DNA (SPED) were added to milk at concentrations ranging from 0.1 to 100 ppb (μg per kg milk). Thereby the milk, as well as the milk-derived products yoghurt and cheese, could be uniquely labeled with a DNA tag. Procedures for the extraction of the DNA tags from the food matrixes were elaborated and allowed identification and quantification of previously marked products by quantitative polymerase chain reaction (qPCR) with detection limits below 1 ppb of added particles. The applicability of synthetic as well as naturally occurring DNA sequences was shown. The usage of approved food additives as DNA carrier (silica = E551) and the low cost of the technology (<0.1 USD per ton of milk labeled with 10 ppb of SPED) display the technical applicability of this food labeling technology.

Locus minimization in breed prediction using artificial neural network approach

Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

Foreign meat identification by DNA breed assignment for the Chinese market

Methods for individual identification are usually employed for traceability, whereas breed identification is useful to detect commercial frauds. In this study, Chinese Yellow Cattle (CYC) samples plus data from six Bos taurus breeds, two Bos indicus breeds, and one composite breed were used to develop an allocation test based on 22 microsatellites. The test allowed discriminating all foreign breeds from the CYC, although some CYC individuals were wrongly allocated as Limousin or Holstein, probably due to the recent introduction of these breeds into China. In addition, CYC evidenced a previously reported Zebu cline (south-north) and a possible structure within the B. taurus component that should be confirmed. An independent test performed with meat samples of unknown breed origin from Argentina allocated 92% of them to either Angus, Hereford, or their crossbreed, but none was identified as CYC. We conclude that the test is a suitable tool to certify meat of foreign breed origin and to detect adulterations of CYC beef labeled as imported meat.