7 research outputs found
Scoring pleurisy in slaughtered pigs using convolutional neural networks
Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry
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Modernising meat inspection using risk-based approaches
A workshop at the SafePork Conference in 2023 delved deeply into the concepts and practicalities of risk-based meat inspection. Such approaches could significantly improve food safety while making the process more efficient and cost-effective
Whole mitochondrial genome sequencing provides new insights into the phylogeography of loggerhead turtles (Caretta caretta) in the Mediterranean Sea
Population structure and phylogeography of the loggerhead sea turtle (Caretta caretta) have so far been assessed mainly by mitochondrial DNA (mtDNA) single-gene sequencing studies. However, phylogenetic relationships amongst matrilines, genetic characterisation of rookeries and mixed-stock analyses have suffered from the limited resolution obtained by comparison of relatively short sequences such as from the mtDNA control region. Whole mitogenome sequencing can significantly improve population genetics, particularly in marine organisms showing female natal philopatry. Despite mitogenomics becoming increasingly common in biodiversity monitoring and conservation, only a few complete mitogenomes are available for C. caretta. In this study, we sequenced the complete mtDNA of 61 loggerhead turtles sampled between 2008 and 2021 along the Italian coastline and central Mediterranean Sea. We assigned complete mtDNA haplotypes to dead embryos and bycatch samples, and introduced a first nomenclature for loggerhead mitogenomes. Analysis of mtDNA diversity, Maximum Parsimony and Bayesian phylogenetic reconstruction allowed improved resolution of lineages with respect to studies reporting on partial mtDNA control region sequence comparisons, and we were able to further inform previous analyses on loggerhead ancestry based on control region haplogroups. Overall, whole mitogenome analysis has potential for considerable improvement of evolutionary history and phylogeographic investigations as well as mixed-stock surveys of loggerhead turtles
A NON-INVASIVE METHOD FOR HEAVY METALS DETERMINATION IN LOGGHERHEAD TURTLE (CARETTA CARETTA ) CARAPACE BY SQUARE WAVE ANODIC STRIPPING VOLTAMMETRY (SWASV)
Applications of computer vision systems for meat safety assurance in abattoirs: A systematic review
Introduction in 2017–2019 of the new EU legislation on official controls in food production allowed use of computer vision systems (CVSs) as complementary tools in meat inspection of bovines, pigs and poultry. A systematic literature review was performed to identify and analyse relevant articles reporting on the performances of CVSs used in abattoirs for ante- and post-mortem veterinary inspection and meat safety assurance, including systems for detecting carcass/organ contamination and lesions. In this review, 62 articles were identified and analysed. There were 35 articles reporting on CVS performance in the detection of carcass/organ lesions and 27 in the detection of carcass contamination. CVSs for broiler chicken, pig and bovine meat safety assurance were reported in 53, 5 and 4 articles, respectively. Not all developed CVSs were validated, and only three articles reported results from real-time evaluation of CVS performance in an abattoir vs performance of the official veterinarian. Most of the reported CVS performance measures (i.e., sensitivity and specificity) were >80%. A high specificity in detecting lesions and carcass contamination (i.e., a low number of false positives) is of importance for the food business operator in order to minimise food waste, whereas a high sensitivity (i.e., a low number of false negatives) is required for production of wholesome and safe meat. At present, the existing CVSs developed for overall meat safety assurance of broiler chicken carcasses and organs demonstrate very high sensitivities but suboptimal specificities, indicating the need for further CVS development and optimisation