One of the most important food safety issues is the detection of mycotoxins, the ubiquitous, natural contaminants in cereals. Hyperspectral imaging (HSI) is a new method in food science, it can be used to predict non-destructively the changes in composition and distribution of compounds. That is why, in the last decade, the potential of HSI has been evaluated in many fields of food science, including mycotoxin research.The aim of the recent study was to test the feasibility of HSI for the differentiation according to the toxin content of cornmeal samples inoculated with Fusarium graminearum, Fusarium verticillioides and Fusarium culmorum and samples with natural levels of mycotoxins. Samples were measured in the near infrared wavelength range of 900–1,700 nm and mean spectra of selected regions of interest of each image were pre-treated using Savitzky-Golay smoothing and standard normal variate (SNV) method. On the spectra, partial least squares discriminant analysis (PLS-DA) was carried out according to the level of contamination. Partial least squares regression (PLSR) method was used to predict deoxynivalenol (DON) content of samples and the cumulative toxin content: the sum of fumonisins (FB1, FB2) and DON content of samples. Based on the promising results of the study, HSI has the potential to be used as a preliminary testing method for mycotoxin content in feed materials.

Grapevine (Vitis vinifera L.) shows morphological plasticity influenced by environmental factors such as radiation and temperature. The effect of row orientation, exposition of leaves and orchard altitude on leaf morphological traits was evaluated. Grapevine cultivar ‘Furmint’ was investigated in this study with the new version of the GRA.LE.D. raster graphic software. The standard OIV (International Organization of Vine and Wine) descriptors were used with additional size parameters. High morphological variability was observed among the leaves collected from 4 different row orientations and 5 levels of expositions. Exposition levels were assigned according to the estimated total radiation collected by leaves at their position. Selected parameters also responded sensitively to changing elevation in the range of 110–289 m. According to the results, traditional leaf morphological investigations performed with machine vision systems may be recommended to reveal significant ecological factors on ampelometric traits.

No abstract