The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R² = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R² = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

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 this study was to examine the thermal denaturation properties of proteins from fresh and frozen pond mussels Anodonta woodiana by differential scanning calorimetry (DSC). The A. woodiana (Lea, 1834) mussels were collected from the bottom of a lake located in West Poland. Three parts of the mussel: adductor muscle (AM), foot muscle (FO) and part of internal organs (IO), were taken for analysis. The DSC technique was used to characterize the stability of the protein system, and SDS-PAGE was applied for the separation and identification of proteins. Thermal analysis in all parts of the mussel revealed two main endothermic transitions: the first at 60–61 °C and the second at 69–70 °C. Denaturation transition occurred in the temperature range from 42 to 76 °C. The highest enthalpy of denaturation was observed for FO and was 15.75 J g⁻¹ (per 1 g of protein) and 14.01 J g⁻¹ for AM. The lower-temperature peak, related to myosin and paramyosin, accounted for approximately 70–80 % of the total area of transition. Electrophoretic analysis (SDS-PAGE) confirmed that the paramyosin, with a mass of approx. 98–107 kDa, constituted the largest percentage, i.e., 24 % in AM and 17 % in IO and FO. Two other main myofibrillar proteins were also present in significant amounts: myosin with a molecular mass of 230–245 kDa (12–14 %) and actin with a mass of 47 kDa (18–20 %). In this study, the effect of freezing on protein stability was also investigated. The results of DSC analyses revealed that the process of freezing did not have a significant influence on the first and second peak temperature. Significant differences in enthalpy between fresh and frozen samples were observed only for AM, for which the total enthalpy (ΔH) decreased by 33 %. This study provided insight into the biochemistry of A. woodiana meat and offers an area of scientific novelty. Recognizing quality properties of meat tissue from freshwater Chinese pond mussels collected from selected water ecosystems seems to be important with respect to the potential possibility of using it for feed, pet food or even in food products.