Ochratoxin A (OTA) is a mycotoxin that is produced after the growth of several Aspergillus and Penicillium spp. in feeds or foods. OTA has been proved to possess nephrotoxic, hepatotoxic, teratogenic, neurotoxic, genotoxic, carcinogenic and immunotoxic effects in animals and humans. OTA has been classified as possibly carcinogenic to humans (Group 2B) by the IARC in 2016. OTA can be mainly found in animals as a result of indirect transmission from naturally contaminated feed. OTA found in feed can also contaminate pigs and produced pork products. Additionally, the presence of OTA in pork meat products could be derived from the direct growth of OTA-producing fungi or the addition of contaminated materials such as contaminated spices. Studies accomplished in various countries have revealed that pork meat and pork meat products are important sources of chronic dietary exposure to OTA in humans. Various levels of OTA have been found in pork meat from slaughtered pigs in many countries, while OTA levels were particularly high in the blood serum and kidneys of pigs. Pork products made from pig blood or organs such as the kidney or liver have been often found to becontaminated with OTA. The European Union (EU) has established maximum levels (ML) for OTA in a variety of foods since 2006, but not for meat or pork products. However, the establishement of an ML for OTA in pork meat and meat by-products is necessary to protect human health.

This paper presents the results of an extensive countrywide investigation performed in 325 dairy sheep flocks and 119 goat herds throughout Greece. The objectives of the study were (a) to investigate fat and protein content in the bulk-tank raw milk of small ruminant farms in Greece and (b) to identify factors potentially influencing that content and factors that can contribute to increased content. The mean fat/protein contents in bulk-tank raw milk of sheep and goats were 6.16 ± 0.05%/4.43 ± 0.01% and 4.77 ± 0.44%/3.23 ± 0.30%, respectively. Significant differences were seen in protein content between farms in the various parts of the country. For sheep, multivariable analyses revealed breed and age of lamb removal from dams as significant for fat content, and somatic cell counts, management system in the farm, administration of anthelmintic treatment during the last stage of pregnancy, and farmer education as significant for protein content. For goats, significant factors were month into lactation period, age of kid removal from dams, and breed for fat content, and somatic cell counts, month into lactation, grazing duration, and % Teladorsagia larvae in faecal samples for protein content. For concurrently high fat and protein content, in multivariable analyses, the following emerged as significant factors: somatic cell counts in milk, numbers of parasite eggs in faeces, and veterinary collaboration (sheep), and month into lactation and somatic cell counts in milk (goats). The results indicate that high somatic cell counts in milk (reflecting the presence of mastitis) and gastrointestinal parasitic infections (mainly Teladorsagia infection) appear to exert a more significant influence on fat and protein content of milk, in comparison to non-infection-related factors.

The aims of the present study were to examine the occurrence ofStaphylococcusspp. in the tonsils of slaughtered pigs in a regional slaughterhouse in Greece, the antibiotic resistance of theStaphylococcusspp. isolates, and the enteroxigenicity of theS. aureusisolates.Staphylococcusspp. were isolated in 70 (48 center dot 61%) out of the total 144 tonsil samples. The predominant species wasS. aureusin coagulase-positive staphylococci (CoPS), while the predominant species wereStaphylococcus epidermidisandStaphylococcus saprophyticusin the coagulase-negative staphylococci (CoNS).Staphylococcusspp. isolates presented high antibiotic resistance frequencies to tetracycline (97 center dot 1%) or clindamycin (80 center dot 0%) and low antibiotic resistance frequencies to fusidic acid (14 center dot 3%). No methicillin-resistantS. aureus(MRSA) strains were identified, and allStaphylococcusspp. isolates were susceptible to vancomycin. Among the 26S. aureusisolates, 21 (80 center dot 76%) possessed staphylococcal enterotoxin genes with seven different enterotoxin gene profiles. The predominant enterotoxin profile wasseg,seiandsejwith sevenS. aureusisolates. The occurrence of multidrug resistantStaphylococcusspp. in pig tonsils indicate public health risk to pork consumers and handlers in developing antimicrobial resistance.

The objective of the present report is the dissemination of information acquired during the application of ISO 6579-1:2017 for the isolation of Salmonella spp. from swine samples. ISO 6579-1:2017 is the protocol officially recommended by the EU for the isolation of Salmonella spp., aiming for the harmonization of effective control of Salmonella infection in food-producing animals. Successful control of animal salmonellosis is highly dependent on the sensitivity and reliability of the biochemical methods used to detect the presence of the pathogen in various stages of food production. Application of ISO 6579-1:2017 resulted in the isolation of twelve Salmonella spp. and eighteen other members of the family of Enterobacteriaceae biochemically and other bacteria antigenically resembling salmonellae. An evaluation of the culture media was conducted, including xylose-lysine-desoxycholate agar (XLD), Salmonella–Shigella Agar (SS), Brilliant Green Agar (BGA), Salmonella Chromogenic Agar (SCA), Triple Sugar Iron (TSI), and modified semi-solid Rappaport–Vassiliadis (MSRV) agar. The evaluation showed that these culture-selective media differed significantly in their performance for the isolation of Salmonella from swine samples. It was concluded that the presence of atypical Salmonella strains negatively affects the prevalence of Salmonella, thus affecting the identification of carrier pigs and eventually affecting the efficiency of control programs. Thus, doubtful results require additional biochemical testing to confirm the accuracy of such universally recommended isolation methods.

The objectives of this study were (i) to describe staphylococcal isolates recovered from bulk-tank raw milk collected from sheep and goat farms during a countrywide study performed in Greece, (ii) to study management factors potentially associated with their presence in bulk-tank milk and (iii) to provide evidence regarding their association with the quality of the milk. In total, 312 staphylococcal isolates, recovered from samples of bulk-tank raw milk from 444 small ruminant farms in Greece, were evaluated in this work. The in vitro formation of biofilm by the isolates was tested by combining the findings of (a) culture appearance on Congo Red agar plates and (b) results of a microplate adhesion test. The most frequently identified species was Staphylococcus aureus (75 isolates); other frequently recovered species were S. simulans (44 isolates), S. equorum (34 isolates) and S. haemolyticus (26 isolates); in total, 23 species were identified. In total, 224 (71.8%) isolates were biofilm-forming and were recovered from the bulk-tank milk samples of 148 sheep flocks (45.5%) and 55 goat herds (46.2%). There was evidence of seasonality in the isolation of staphylococci: during spring, mostly biofilm-forming isolates were recovered, whilst during summer, mostly non-biofilm-forming isolates were recovered. Among farms applying machine-milking, the proportion of farms from which biofilm-forming isolates were recovered was higher where water with temperature < 50 °C or ≥90 °C was used to clean the milking parlour. In the multivariable analyses, for farms applying machine-milking, the temperature of the water emerged as the only significant variable (p = 0.024), whilst in farms applying hand-milking, the only tendency that emerged was for the frequency of collection of milk from the farm tank (p = 0.08). In sheep flocks, recovery of biofilm-forming staphylococci from the bulk-tank milk was associated with higher somatic cell counts and higher total bacterial counts in the milk. The study identified abiotic factors related to the presence and isolation of these bacteria, specifically the temperature of water used for the cleaning of the milking parlour (in farms where machine-milking is applied) and the frequency of milk collection from the farm tank. These factors apply after the production of milk, and they could thus be regulated appropriately in order to reduce bacterial load and improve the quality of milk delivered to dairy plants. In sheep farms, an association was also seen between recovery of biofilm-forming staphylococci and high somatic cell counts in milk.

The specific objective of the present study was to develop computational models, by means of which predictions could be performed regarding the quality of the bulk-tank milk in dairy sheep and goat farms. Our hypothesis was that use of specific variables related to the health management applied in the farm can facilitate the development of predictions regarding values related to milk quality, specifically for fat content, protein content, fat and protein content combined, somatic cell counts, and total bacterial counts. Bulk-tank milk from 325 sheep and 119 goat farms was collected and evaluated by established techniques for analysis of fat and protein content, for somatic cell counting, and for total bacterial counting. Subsequently, computational models were constructed for the prediction of five target values: (a) fat content, (b) protein content, (c) fat and protein, (d) somatic cell counts, and (e) total bacterial counts, through the use of 21 independent variables related to factors prevalent in the farm. Five machine learning tools were employed: decision trees (18 different models evaluated), random forests (16 models), XGBoost (240 models), k-nearest neighbours (72 models), and neural networks (576 models) (in total, 9220 evaluations were performed). Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. In sheep farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 3.95%); for the prediction of fat and protein content combined, neural networks was selected (6.00%); and for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (6.55%); no tool provided useful predictions for fat content and for total bacterial counts. In goat farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 6.17%); for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (4.93% and 5.00%); and for the prediction of total bacterial counts, neural networks was selected (8.33%); no tool provided useful prediction models for fat content and for fat and protein content combined. The results of the study will be of interest to farmers, as well as to professionals; the findings will also be useful to dairy processing factories. That way, it will be possible to obtain a distance-aware, rapid, quantitative estimation of the milk output from sheep and goat farms with sufficient data attributes. It will thus become easier to monitor and improve milk quality at the farm level as part of the dairy production chain. Moreover, the findings can support the setup of relevant and appropriate measures and interventions in dairy sheep and goat farms.