Modern Methods of Pre-Treatment of Plant Material for the Extraction of Bioactive Compounds

In this review, recent advances in the methods of pre-treatment of plant material for the extraction of secondary metabolites with high biological activity are presented. The correct preparation of the material for extraction is as important as the selection of the extraction method. This step should prevent the degradation of bioactive compounds as well as the development of fungi and bacteria. Currently, the methods of preparation are expected to modify the particles of the plant material in such a way that will contribute to the release of bioactive compounds loosely bonded to cell wall polymers. This review presents a wide range of methods of preparing plant material, including drying, freeze-drying, convection drying, microwave vacuum drying, enzymatic processes, and fermentation. The influence of the particular methods on the structure of plant material particles, the level of preserved bioactive compounds, and the possibility of their release during the extraction were highlighted. The plant material pre-treatment techniques used were discussed with respect to the amount of compounds released during extraction as well their application in various industries interested in products with a high content of biologically active compounds, such as the pharmaceutical, cosmetics, and food industries.

Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks

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.