No abstract

Fruits represent a valuable source of bioactivity, vitamins, minerals and antioxidants. They are often used in research due to their potential to extend sustainability and edibility. In this research, the currants were used to obtain currant powders by dehumidified air-assisted spray drying. In the research analysis of currant powders, advanced machine learning techniques were used in combination with Lab color space model analysis and Fourier transform infrared spectroscopy (FTIR). The aim of this project was to provide authentic information about the qualities of currant powders, taking into account their type and carrier content. In addition, the machine learning models were developed to support the recognition of individual blackcurrant powder samples based on Lab color. These results were compared using their physical properties and FTIR spectroscopy to determine the homogeneity of these powders; this will help reduce operating and energy costs while also increasing the production rate, and even the possibility of improving the available drying system.

No abstract

The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.

No abstract