The effect of convective drying temperature (T_d), air velocity (v), rehydration temperature (T_r), and kind of rehydrating medium (pH) was studied on the following apple quality parameters: water absorption capacity (WAC), volume ratio (VR) color difference (CD). To model, simulate, and optimize parameters of the drying and rehydration processes hybrid methods artificial neural network and multiobjective genetic algorithm (MOGA) were developed. MOGA was adapted to the apple tissue, where the simultaneous minimization of CD and VR and the maximization of WAC were considered. The following parameters range were applied, 50 ≤ T_d ≤ 70 °C and 0.01 ≤ v ≤ 6 m/s for drying and 20 ≤ T_r ≤ 95 °C for rehydration. Distilled water (pH = 5.45), 0.5% solution of citric acid (pH = 2.12), and apple juice (pH = 3.20) were used as rehydrating media. For determining the rehydrated apple quality parameters the mathematical formulas were developed. The following best result was found. T_d = 50.1 °C, v = 4.0 m/s, T_r = 20.1 °C, and pH = 2.1. The values of WAC, VR, and CD were determined as 4.93, 0.44, and 0.46, respectively. Experimental verification was done, the maximum error of modeling was lower than 5.6%.

Fruit and vegetable processing has a significant impact on the environment due to its consumption of a significant amount of water. Water consumption mainly depends on the type of production and the technology used. Water in fruit and vegetable processing plants is used as a raw material, an energy carrier, and in hydro transport, as well as for washing raw materials and maintaining production hygiene. The variety of technological operations carried out in the production process and the seasonality of production make it difficult to objectively assess the use of water in fruit and vegetable processing plants. Few available publications in this field provide numerical values of water unit consumption indices, with none entering into the cause-and-effect relationships of water use in plants in this industry. The aim of this study was to analyze the research to date and to verify the following research hypothesis: the structure of processing and the relationship between the weights of individual products have an impact on water consumption in fruit and vegetable processing plants. For this purpose, neural models of water consumption were developed for the largest agri-food processing plants in Poland that use similar technology. Water consumption was then optimized using genetic algorithms for the processing structure. The results confirmed the hypothesis that production structure has a significant impact on the rationalization of water consumption. The optimization results show that the production of concentrates, juices, and drinks has the greatest impact on water consumption. The lowest water consumption will be achieved when the production of concentrates is at a 2 to 1 ratio to the production of juices and drinks.

The purpose of the study was to establish relationship between different sensory attributes of wild boar meat, as well as to develop a prediction model of sensory attributes demanded. The sensory analysis of 40 samples of wild boar meat (the loin) was performed. For wild boar meat, tenderness, juiciness, colour, taste, aroma, and off-flavours are significantly correlated with general quality of meat, assessed by the sensory panel. The results from the study indicate that wild boar meat reveals characteristic sensory traits; however, texture as well as off-flavours do not play an important role in creating general quality, but the most important factors influencing the general quality of wild boar meat included juiciness, colour, taste, and aroma.