Introduction: A model of fatty liver in postpartum sheep was established to measure blood paraoxonase 1 (PON1) and other biochemical indicators, which were used to predict fatty liver in sheep. Material and Methods: Sheep were assigned into two experimental groups: a fatty liver group (T, n = 10) and a healthy control group (C, n = 5). PON1 enzyme activity towards paraoxon as a substrate was quantified spectrophotometrically. The results were analysed by t-test and pearson correlation coefficient. Disease was predicted by binary logistic analysis, and diagnostic thresholds were determined by receiver operatingcharacteristic (ROC) analysis. Results: The activity of serum PON1 in group T was significantly decreased (P < 0.05) when compared with C group, and liver lipid content and the levels of serum BHBA, NEFA, and TG were significantly increased (P < 0.05). Thresholds were lower than 74.0 U/mL for PON1, higher than 0.97 mmol/L for β-hydroxybutyrate, higher than 1.29 mmol/L for non-esterified fatty acids, higher than 0.24 mmol/L for triglycerides, and lower than 71.35 g/L for total protein. Conclusion: This study verified that PON1, BHBA, NEFA, TG, and TP could be used to predict the risk of fatty liver in sheep.

Introduction: The purpose of the study was to determine and model the growth rates of L. monocytogenes in cooked cured ham stored at various temperatures. Material and Methods: Samples of cured ham were artificially contaminated with a mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12, or 15°C for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14, and 16 days. A series of decimal dilutions were prepared from each sample and plated onto ALOA agar, after which the plates were incubated at 37°C for 48 h under aerobic conditions. The bacterial counts were logarithmised and analysed statistically. Five repetitions of the experiment were performed. Results: Both storage temperature and time were found to significantly influence the growth rate of listeriae (P < 0.01). The test bacteria growth curves were fitted to three primary models: the Gompertz, Baranyi, and logistic. The mean square error (MSE) and Akaike’s information criterion (AIC) were calculated to evaluate the goodness of fit. It transpired that the logistic model fit the experimental data best. The natural logarithms of L. monocytogenes’ mean growth rates from this model were fitted to two secondary models: the square root and polynomial. Conclusion: Modelling in both secondary types can predict the growth rates of L. monocytogenes in cooked cured ham stored at each studied temperature, but mathematical validation showed the polynomial model to be more accurate.