Artificial neural networks in the evaluation of the influence of the type and content of carrier on selected quality parameters of spray dried raspberry powders

•Image analysis was carried out by exploiting a trained artificial neural network.•Spray drying with suitable selection and share of carrier can be an effective solution.•Texture analysis could be used instead of physicochemical analysis for identification of spray-dried. In the paper an attempt was made to evaluate influence of type (maltodextrin, gum Arabic and inulin) and content (50, 60, 70% solids) of carrier on quality of raspberry powders. The different types of powders were compared taking into account the structure of microparticles, water activity and their humidity. The use of modern methods such as: low temperature spray drying, artificial intelligence together with visual technique supported by electron microscope are undoubtedly an innovation in this solution. The aim of this undertaking is monitoring the process in order to obtain raspberry powders characterized by high quality characteristics. The paper shows the created neural models, which allow to obtain homogeneity on the basis of microparticles of powders as well as their microbiological condition (indirectly via water activity and humidity). The created networks were characterized by low Root Mean Square and high effectiveness of classification on the level of 99%.