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Mushrooms and fungi not only present a fascinating world of shapes, both macro- and microscopic, but they are also an interesting source of flavours, fragrances and odours, e.g. garlic, coconut, flour-like, cucumber or fruit-like, as well as the most characteristic for this kingdom of living organisms mushroom-like flavour and aroma. Fungi can possess many different and interesting flavours and fragrances – starting from nice anise-like, fruit-like, cucumber, garlic, to cheese-garlic, and ending with potato or flour-like smells. Some mushrooms emit carbide or distinctly faecal-like odour. The taste of mushrooms is frequently correlated with their aroma. What components does the core of a mushroom flavour consist of? Chemical analysis of specimens reveals compounds responsible for characteristic flavour and odour. It was found that the most characteristic flavour compound is defined mainly by C8 volatiles. Between all C8 compounds the most important for mushroom flavour are oct-1-en-3-ol, octan-3-ol, octan-3-on and oct-1-en-3-on. Fungi and mushrooms can enable biotechnological production of some flavour components, for instance the Nidula niveotomentosa produces a characteristic raspberries compound – raspberry ketone in submerged cultures; the biotechnological production can also provide rare and tasty forest mushroom biomass e.g. edible boletus.

Alterations in some physiological processes in source leaves of sugar beet-such as chlorophyll and carbohydrate concentrations, stomatal conductance, rate of net photosynthesis and transpiration, and activity of the photosynthetic apparatus during root interaction with Aphanomyces cochlioides, were investigated. The influence of time of infection on plant health, yield quality and quantity was also examined. Plants were infected at different times of their growth period: on the sowing day and 4 or 8 weeks after sowing. A variation treatment, with nonpelleted seeds infected on the sowing day, was also analyzed. The experiment showed that development of disease symptoms depends on the time of infection and seed protection. A significant root yield decrease was observed in case of late infection, as compared to the yield of plants infected on the sowing day. The fresh weight of leaves was significantly increased where there was late infection. The infected plants showed a lower content of K + , Na + and α-amino-N than did the controls. Infection by A. cochlioides induced chlorophyll degradation mostly in older leaves with the occurrence of natural senescence processes. Chlorophyll fluorescence parameters indicated that the photosynthetic apparatus of younger leaves was more sensitive to pathogen infection, when compared to older ones. The photochemical efficiency of photosystem II was reduced in young leaves mainly due to disturbance of the water-splitting system. In plants grown from non-pelleted seeds a strong impairment of PSII was observed only in those leaves which developed during early pathogen infection. In young leaves of plants infected in the fourth week after sowing, inhibition of the rate of net photosynthesis was correlated with the increase in intercellular CO 2 concentration, indicating some disturbance in the carbon assimilation phase. In mature leaves of late infected plants the reduction of photosynthesis net rate was associated with a decrease of stomatal conductance and an increase of diffusion resistance to CO 2 and H 2 O, which was also the cause of the transpiration rate inhibition. When the leaves developed during early infection, an increase of specific leaf weight and accumulation of carbohydrates was observed. In mature leaves of nonprotected plants infected on the sowing day, the recovery of all physiological processes was observed together with a diminution of disease symptoms.

Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350–2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.

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