valuable seeds and identification of foreign materials are very important in
optimization of cleaning process and plant development. Digital image
processing was applied to measure statistical parameters of surface patterns,
such as mean, entropy, angular second momentum and contrast. In addition, polar
quality points of histograms of intensity differences were also computed. A
distance function of dimensionless quantity was introduced and applied in
classification. Distances were calculated from average value and standard
deviation of parameters. Advantages of this method are the easy calculation and
assignment of probabilities to distance values. Not only the grey level
differences were collected, the method was extended to colour signals (red,
green and blue) as well. Analysis of colour information improved effectiveness
Radiotracer method was developed for measuring14CO2 content in cement-wood mixtures. The carbon dioxide used for the treatment was labeled with14C, a -emitting radioisotope, and samples were measured by -scintillation and liquid scintillation techniques. Test samples were prepared in the laboratory with various compositions and treated with labeled14CO2. The tracer was released from Ba14CO3 by lactic acid with total activity of 37 MBq. Selectivity of the technique allows to distinguish the carbon dioxide bound during the treatment and bound previously from the mbient air. Sensitivity of the method is higher than that of traditional methods and allowed determination of CO2 content in each component of the mixture. It is 63 ng. if measured by -scintillation detector and 1.6 ng, if measured by liquid scintillation. Accuracy of the method is 0.3%.
The fat content (fat distribution) of the pork and beef raw material is one of their most important quality characteristics. Image processing methods were applied to provide with quantitative parameters related to these properties. Different hardware tools were tested to select the appropriate imaging alternative. Statistical analysis of the RGB data was performed in order to find appropriate classification function for segmentation. Discriminant analysis of the RGB data of selected image regions (fat-meat-background) resulted in a good segmentation of the fat regions. Classification function was applied on the RGB images of the samples, to identify and measure the regions in question. The fat-meat ratio and textural parameters (entropy, contrast, etc.) were determined. Comparison of the image parameters with the sensory evaluation results showed an encouraging correlation.
Experiments were performed to follow the moisture loss of apple slice samples (discs of 3 cm diameter and 1 cm thickness) during the drying process. Different optical methods were tested in order to find a model for prediction of the moisture content based on non-contact measurements. Apple discs were dried in a hot air drying chamber for different periods (0–7 hours). The mass of every individual sample was measured before drying (initial mass), after drying (actual mass), and after the optical tests at the end of a 24 h drying process (final mass). Both the wet base and the dry base moisture content were calculated from the actual mass and the final mass of the samples. The optical properties of the samples of different moisture content were measured by two different optical methods. Laser induced backscattering method, applying 3 mW laser modules of different wavelengths, was used to generate the diffuse reflection pattern (halo) on the surface of samples and evaluate the halo properties with a machine vision system. Near Infrared Reflection (NIR) technique was also used to collect/measure the log(1/R) spectra of the samples in the 740–1700 nm range.PLS method with full cross-validation was used to predict the moisture content of the samples based on the backscattering data (quantitative parameters of the halo profile) and on the NIR spectra (raw and transformed log(1/R) data). Effective models (r>0.98, RPD>5) were found for prediction of the dry base moisture content of the samples based on both optical methods.
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.
From the nineteen-nineties, cobweb disease caused serious losses for the mushroom sector in Europe, in the USA, and in Australia (Fletcher & Gaze, 2008), so it is one of the most notable fungal infections of cultivated white button mushroom (Agaricus bisporus). The aim of this study was to identify cobweb disease (Cladobortyum dendroides) caused cap spotting and brownish rot on the mushroom sporocarp, and to find a proper discrimination method in the case of this infection.Fruiting body samples were divided into 4 groups, a control one and three others treated with different chemicals that are tested against fungal infections. The groups were subdivided into 2 portions and the first was infected with cobweb disease. Images of the caps were recorded and their hyperspectral images were acquired in the wavelength range of 900–1700 nm.On the hyperspectral images infected and healthy areas were selected, on these average spectra differences were found around the known water peaks (1200 and 1450 nm). The spatial distribution of the water content can be used for the detection of the spoilage, because the infected areas showed different reflection values at these water absorption peaks.Support Vector Machine method was applied successfully to discriminate between the infected and control groups and Monte Carlo cross-validation was carried out.
Neutron activation analysis, gamma-ray spectrometry, particle sizing and photography of the corrosion products were used to qualify the constructional materials and primary coolants. The investigations related to: characterization of the construction materials of the primary circuit, circulated washing, hot conditioning, physical and energetic start up. The aim of the measurements was to study the cleanness of the primary circuit from assembling up to energetic start up and to follow closely the variation in the amount and the removing of the mechanical contaminants and corrosion products depending on the technological parameters.
The aims of our research work were the investigation of postharvest changes of pear samples (Pyrus communis cv. Bosc kobak) during combined cold storage and shelf-life (storage at room temperature), the determination of quality changes by mainly non-destructive methods, the modeling of the changes of the non-destructive parameters (acoustic, impact stiffness coefficient, chlorophyll fluorescence parameters [Fv/Fm, Fm/F0]), and multivariate statistical analysis of the measured and predicted data based on the data of the non-destructive texture analysis (acoustic and impact methods), chlorophyll fluorescence analysis and laser scattering measurement. Storage Time Equivalent Value (STEV) was calculated and introduced based on mass-loss analysis. The changes of the non-destructive parameters were analyzed vs. this virtual storage time (STEV). The changes of acoustic, impact stiffness coefficient and chlorophyll fluorescence parameters can be predicted by exponential function. The predicted time constants of the parameters were 21.0, 45.8, 47.1, 83.4, acoustic, impact stiffness coefficient, Fm/F0, Fv/Fm, respectively. The lower the time constant, the quicker is the change of the given parameter during storage, the higher is its sensitivity. By this point of view, the percentage mass loss related sensitivity to the characterization of textural changes, the predicted acoustic stiffness coefficient was found to be more sensitive than the impact stiffness coefficient. The Fm/F0 value characterized more sensibly the changes of the chlorophyll fluorescence than in the literature commonly used Fv/Fm. The non-contact laser scattering method based significant PLS models were constructed to predict the quality related pear characteristics (mechanical properties, chlorophyll fluorescence parameters).
Grapevine berry shape has important marketing value in the table grape commerce, hence variability evaluation of this characteristic is highly important. In this study berry shape of 5 table grape genotypes: “Fanny”, “Lidi”, “Hamburgi muskotály”, “Moldova”, and “Orsi” were compared. To evaluate the shape variability graphic reconstruction and elliptic Fourier analysis have been carried out. Shape outlines have been investigated and Principal Component Analysis (PCA) has been performed with the SHAPE software package. PCA of the contours showed that 6 out of the 77 principal components were effective to describe shape attributes. The first 6 PCs explained 94.51% of the total variance. PC1 associated with the width and length of the berry. PC2 related to the shape of the top and bottom of the berries, while PC3 linked to the ratio of the top and the bottom width. ANOVA of the principal component scores revealed significant difference among the genotypes. Results suggest that morphology of the berry is a variable not only among but within the accessions. Our findings confirmed that elliptic Fourier descriptors (EFDs) would be a powerful tool for quantifying grapevine berry morphological diversity.
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.