Some of feedstuffs used as raw materials in feed industry contain anti-nutritional factors that negatively influence their quality. One of them is soybean, which is, prior to oil extraction, referred to as full-fat soybean (FFSB). Anti-nutritional factors in raw FFSB can be destroyed by moderate heating, but both over- and under heat processing limits the availability of soybean amino acids. Among laboratory procedures that are available for assessing the degree of FFSB heat treatment, two methods, i.e. Protein dispersibility index (PDI) and protein solubility in potassium hydroxide (PSKOH), are based on protein solubility, which was claimed to be the most reliable indicator of the degree of FFSB heat treatment. This paper presents the results of an inter-laboratory study conducted to establish precision of the PDI and PSKOH methods by determining their reproducibility limits. Five samples of FFSB were heat-treated at temperatures between 110 and 164 °C and analyzed by six laboratories for PDI and PSKOH. Established reproducibility limit for PDI method of 8.87 index units found in this study appeared to be too wide, indicating a low precision of this method. PSKOH method produced very good reproducibility limit of 8.56% and could be recommended as a preferred method for FFSB quality control in feed laboratories

Energy value of diets has importance for feed producers and farmers. Methods for in vivo determination of metabolisable energy have high accuracy, but they are time and cost consuming. The aim of this study was to investigate the effect of enzymatic digestible organic matter and values of proximate chemical analysis on prediction of the nitrogen corrected true metabolisable energy (TME n) of diets for broilers. The performance of Artificial Neural Network was compared with the performance of first order polynomial model, as well as with experimental data in order to develop rapid and accurate method for prediction of TME n content. Analysis of variance and post-hoc Tukey"s HSD test at 95% confidence limit have been calculated to show significant differences between different samples. Response Surface Method has been applied for evaluation of TME n. First order polynomial model showed high coefficients of determination (r 2 = 0.859). Artificial Neural Network model also showed high prediction accuracy (r 2 = 0.992). Principal Component Analysis was successfully used in prediction of TME n .

Natural environment represents a dynamic bioreactor with numerous chemical, biochemical and microbiological processes through which harmful materials are destroyed, so that living organisms and human beings are not endanger. Controlled anthropogenic actions can assist the natural ecosystem to become an efficient bioremedial unit and to reduce the level of effluents produced in the biotechnological transformations during massive food production. In this study, a monitoring system for the chemical oxygen demand (COD) and the heavy metal levels in water was established, followed by construction and building of a precipitator in order to prevent discharging of sludge. The results contribute to the hypothesis of existence of in situ bioremedial processes in the observed ecosystem. The significant influence of the precipitator on the decrease of pollution was demonstrated: a decrease of both the COD value and the heavy metal levels downstream from the precipitator for about 15%. Therefore it can be concluded that the precipitator significantly contributes to the ecosystem by the reduction of pollutant level