Cricket powder (CP) contains significant amounts of protein, fat (including unsaturated fatty acids), and fiber, as well as vitamins and minerals. The high nutritional value and low price make it an interesting addition to food production. This paper is a report on the results of the addition of cricket powder to pasta. Three levels of durum semolina replacement were chosen: 5%, 10%, and 15%. The obtained products were analyzed for their nutritional composition, cooking and textural properties, and color, as well as consumer acceptance. The results indicate that the addition of CP influenced the cooking weight and cooking loss (reducing losses and water absorption), as well as the color of the pasta, reducing its lightness and shifting color balances to blue and red. The firmness of pasta was also influenced. The firmness was strengthened by addition of CP. Principal components analysis indicated that the flavor change had the most pronounced effect on consumer acceptance. Nevertheless, sensory evaluation proved that protein-enriched pasta produced with CP has consumer acceptance comparable with that of conventional products.

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.

Oilcakes from the oilseed industry are rich in dietary fibre and protein by-products. We assessed the impact of wheat flour replacement with raspberry and strawberry oilcakes on the proximate composition of bread, colour, texture and water behaviour. The substitution influenced the ash, fat and protein content causing an increase in the content of each of the analysed macronutrients. The crumb colour components (CIE L*a*b*) were shifted toward red while the saturation of yellow decreased. Texture analysis showed that the hardness and chewiness of crumb with oilcakes increased as well as springiness decreased. It was found that flour substitution with oilcakes limited significantly water transport and also influenced the molecular dynamics of water in the bread crumb. 1 H NMR measurement results of relaxation times demonstrated that the free water in relation to the bound water in the examined systems depended on the amount of the flour replaced by the oilcake, as well as on the botanical origin of the oilcakes.

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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