Modern technologies with artificial intelligence are widely used in processing industry and serve, among other things, as boost to increase efficiency and automatization of production processes, which in turn allows to increase productivity of companies while at the same increasing their competitiveness. At present a real challenge for this branch of economy is manufacturing farm and food products, characterized by best parameters in terms of quality while maintaining optical production and distribution costs of biological material which is subject to processing. The given paper touches upon the subject related to evaluation of changes of quality of dried meat made from turkey meat in the process of microwave and vacuum drying as well as its influence on the quality of meat, microstructure and sensory features.
In recent years, requirements on potato tuber quality and minimization of chemical protection measures have steadily increased. Increasingly, the precautionary measures in the protection of potato during its vegetation, using the dressing of seed material, are increasingly taking place. Growing awareness of potato producers and increasingly restrictive plant protection standards force the use of new technologies. The quality of the process of applying chemicals to the surface of tubers of seed potato material affects the subsequent quantity and quality of the crop. The aim of the study was to validate the method of evaluating the quality of seed dressing coverage in the process of spraying tubers with a chemical based on computer image analysis.
Self-Organizing Feature Map (SOFM), has been used for the qualitative identification of strawberry juice powders. The research was based on image recognition using powders obtained through an industrial spray-drying process. Results demonstrated that the color features were able to effectively distinguish the research material consisting of spray-dried powders of strawberry juice. The adequate model in terms of the lowest error value RMS (Root Mean Square) contained 46 neurons in the input layer and neurons in the output layer. The model is an effective tool for classifying wrong color changes in strawberry powders.