Прегледај по Аутор "Pezo, Lato"

Сада се приказује 1 - 2 од 2
  • Сличица
    Ставка
    Effect of Atmospheric Cold Plasma Treatments on Reduction of Alternaria Toxins Content in Wheat Flour
    (MDPI, 2019) Janić Hajnal, Elizabet; Vukić, Milan; Pezo, Lato; Orčić, Dejan; Puač, Nevena; Škoro, Nikola; Milidrag, Ardea; Šoronja Simović, Dragana
    Beside Fusarium toxins, Alternaria toxins are among the most commonly found mycotoxins in wheat and wheat products. Currently, investigations of possibilities of reduction of Alternaria toxins in the wheat-processing chain are limited. Therefore, the aim of this study was to explore the potency of cold atmospheric plasma treatments, as a new non-thermal approach, for reduction of alternariol (AOH), alternariol monomethyl ether (AME) and tentoxin (TEN) content in spiked white wheat flour samples. Samples were treated with plasma generated in the air during 30 s to 180 s, with an increment step of 30 s, and at four varying distances from the cold plasma source (6 mm, 21 mm, 36 mm and 51 mm). The reduction of the Alternaria toxins content in samples after treatment was monitored by high performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The maximum reduction of the examined Alternaria toxins was obtained by treatment performed at 6 mm distance from the plasma source, lasting 180 s, resulting in reductions of 60.6%, 73.8% and 54.5% for AOH, AME and TEN, respectively. According to the obtained experimental results, five empirical models in the form of the second-order polynomials were developed for the prediction of AOH, AME and TEN reduction, as well as the temperature and the moisture content of the wheat flour, that gave a good fit to experimental data and were able to predict the response variables successfully. The developed second-order polynomial models showed high coefficients of determination for prediction of experimental results (between 0.918 and 0.961).
  • Сличица
    Ставка
    The prediction of lean meat and subcutaneous fat with skin content in pork cuts on the carcass meatness and weight
    (Springer, 2019) Tomović, Vladimir; Pezo, Lato; Jokanović, Marija; Tomović, Mila; Šojić, Branislav; Škaljac, Snežana; Vujadinović, Dragan; Ivić, Maja; Djekić, Ilija; Tomašević, Igor
    Early post-mortem, objective and non-destructive prediction of tissue distribution in the major pork cuts is a challenge for the meat industry. Mathematical models to predict pig carcass composition using total lean meat percentage and carcass weight were evaluated in this study. The data were obtained from 455 cold pig carcasses which were dissected according to the EU reference method; total lean meat percentage and carcass weight ranged from 42.45 to 69.21% and from 23.26 to 55.22 kg, respectively. Developed empirical models gave a reasonable fit to the experimental data and successfully predicted the carcass composition and tissue distribution in primal cuts. The second order polynomial models showed high coefficients of determination for prediction of experimental results (between 0.612 and 0.929), while the artificial neural network (ANN) model, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, showed better prediction capabilities (overall r2 was 0.889). The newly developed software, based on ANN model is easy, fast, cheap and with sufficient precision for application in the meat industry.