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The aim of the article was to present the effects of lignin grafted with polyvinylpyrrolidone (PVP) as a microbial carrier in anaerobic co-digestion (AcoD) of cheese (CE) and wafer waste (WF). Individual samples of waste cheese and wafers were also tested. The PVP modifier was used to improve the adhesive properties of the carrier surface. Lignin is a natural biopolymer which exhibits all the properties of a good carrier, including nontoxicity, biocompatibility, porosity, and thermal stability. Moreover, the analysis of the zeta potential of lignin and lignin combined with PVP showed their high electrokinetic stability within a wide pH range, that is, 4-11. The AcoD process was conducted under mesophilic conditions in a laboratory by means of anaerobic batch reactors. Monitoring with two standard parameters: pH and the VFA/TA ratio (volatile fatty acids-to-total alkalinity ratio) proved that the process was stable in all the samples tested. The high share of N-NH 4 + in TKN (total Kjeldahl nitrogen), which exceeded 90% for WF+CE and CE at the last phases of the process, proved the effective conversion of nitrogen forms. The microbiological analyses showed that eubacteria proliferated intensively and the dehydrogenase activity increased in the samples containing the carrier, especially in the system with two co-substrates (WF+CE/lignin) and in the waste cheese sample (CE/lignin). The biogas production increased from 1102.00 m 3 Mg −1 VS (volatile solids) to 1257.38 m 3 Mg −1 VS in the WF+CE/lignin sample, and from 881.26 m 3 Mg −1 VS to 989.65 m 3 Mg −1 VS in the CE/lignin sample. The research results showed that the cell immobilization on lignin had very positive effect on the anaerobic digestion process.

In this paper, the authors used an acoustic wave acting as a disturbance (acoustic vibration), which travelled in all directions on the whole surface of a dried strawberry fruit in its specified area. The area of space in which the acoustic wave occurs is defined as the acoustic field. When the vibrating surface—for example, the surface of the belt—becomes the source, then one can observe the travelling of surface waves. For any shape of the surface of the dried strawberry fruit, the signal of travelling waves takes the form that is imposed by this irregular surface. The aim of this work was to research the effectiveness of recognizing the two trials in the process of convection drying on the basis of the acoustic signal backed up by neural networks. The input variables determined descriptors such as frequency (Hz) and the level of luminosity (dB). During the research, the degree of crispiness relative to the degree of maturity was compared. The results showed that the optimal neural model in respect of the lowest value of the root mean square turned out to be the Multi-Layer Perceptron network with the technique of dropping single fruits into water (data included in the learning data set Z2). The results confirm that the choice of method can have an influence on the effectives of recognizing dried strawberry fruits, and also this can be a basis for creating an effective and fast analysis tool which is capable of analyzing the degree of ripeness of fruits including their crispness in the industrial process of drying fruits.

The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high (“H”) and low (“L”) level of saccharification a chosen carrier (potato maltodextrin).

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Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.

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The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called ’PID system’ was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features.