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  • Author or Editor: J. Felföldi x
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Authors: A. Blázovics and J. Felföldi
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The effect of organic growing was studied on the storability of apple cv. Jonica, Liberty, Mutsuand Pinova. Fruits from integrated and organic orchards were stored at 2-4 °C, 95-99% relative humidity for 6 months. Firmness, activity of b-galactosidase and polygalacturonase enzymes were examined. There was no difference in the activity of β-galactosidase and polygalacturonase enzymes at harvest between the organic and integrated apples, but a significant difference was noted between the cultivars except for Mutsu and Pinova. The activity of b-galactosidase enzyme increased significantly during storage except for cv. Pinova and that of polygalacturonase enzyme also increased significantly. The difference in the activity of polygalacturonase became significant between the cultivars during storage except for cv. Jonica and Pinova. The firmness decreased significantly during storage, with the least change in case of cv. Liberty. It can be established that there is, in general, neither a considerable difference between the growing systems nor between varieties at harvest. The differences became higher during storage. It can be stated that the effect of cultivar on the storability is much more considerable than the effect of growing system.

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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.

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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.

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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.

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Testing of two methods novel to ultrasonic measurements was carried out on cheese samples to estimate the Time-of-Flight (TOF) parameter. The Short Time Average/Long Time Average (STA/LTA) method and the Autoregressive Akaike Information Criterion Picker (AR-AIC picker) method are used mainly in seismology for earthquake event detection. The STA/LTA method proved to be ineffective with such noise level that is present during ultrasonic measurements, but the AIC picker algorithm yielded reliable results. A new approach for classification was tested on two types of samples, those were matching in composition, but different in treatment and texture. The method used is based on the results of wavelet decomposition, and after retrieving sufficient spectral data, a linear discriminant analysis (DA) resulted in 100% correct classification, which was compared to the DA classification results based on other methods.

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In the case of organic production the quality assessment of the fruits and vegetables is especially important. Monitoring of the maturation and ripening process, early detection of diseases, decision about harvest date and postharvest treatment need reliable, objective and – preferably – non-destructive quality testing methods. Dynamic hardness or stiffness measurement methods (resonance, impact, wave propagation) offer very useful tools in this field, but with strong limitations in applicability area and/or physical interpretation of the measured parameters. Our objective was to develop method and appropriate portable instrumentation to measure surface hardness – as quality measure – with a nondestructive method.

The computer controlled instrument has an electromagnetically excited impactor fitted with a piezoelectric acceleration sensor, a signal conditioner and A/D converter. To ensure the uniform contact behavior (contact area) between the impactor and the tested produce of wide range of shape, spherical head was applied. Conclusively, Hertz contact theory is to be applied for evaluation of the impact signal. Instead of using empirical “hardness index” – as in the case of several existing instruments – our objective was the physical interpretation of the contact phenomena. The measured acceleration signal was mathematically processed to calculate real physical parameters (force, speed, deformation), and to characterize the process similarly to the widely used texture analyzers, penetrometers. A new hardness parameter – “dynamic elastic modulus” – was introduced. According to the methodological investigations, the measurement was found to be perfectly non-destructive for a wide range of products. Conclusively, the developed method offers a useful tool for quality evaluation of organic horticultural products.

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In this work, the application of an electronic tongue (ET) based on a specific ion-selective sensor array for discrimination of different white wine types is presented. The electronic tongue equipped with specific sensor array containing seven IFSET sensors was used to analyze wine samples. The obtained ET responses were evaluated using different pattern recognition methods. Principal component analysis (PCA) provides the possibility to identify some initial patterns. Linear discriminant analysis (LDA) was used to build models to separate white wine samples based on wine regions and grape cultivars. The results showed that every group was distinguished from each other with no misclassification error. Furthermore, the sequence of the wine sample groups was similar to the increasing total acidity content. Partial least square (PLS) regression was used to build models for the prediction of the main chemical compositions of the wine samples based on the electronic tongue results. The closest correlation (R2=0.93) was found in case of ‘total acidity’, and the prediction error (RMSEP) was 6.9%. The pH of the wine samples was predicted with good correlation (R2=0.89) but higher prediction error (RMSEP=10.71%) from the electronic tongue results. The ET combining these statistical methods can be applied to determine the origin and variety of the wine samples in easy and quick way.

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Authors: E. Várvölgyi, T. Werum, L. Dénes, J. Soós, G. Szabó, J. Felföldi, G. Esper and Z. Kovács

Time consuming and expensive methods have been applied for detection of coffee adulteration based on the literature. In the present work, an optical method (vision system) and the application of an electronic tongue is proposed to reveal the addition of barley in different proportion to coffee in ground and brewed forms. In a range of 1 to 80% (w/w) Robusta coffee was blended with roasted barley. Principal Component Analysis (PCA) accomplished on vision system image data showed a good discrimination of the adulterated samples. The results of Polar Qualification System (PQS) data reduction method revealed even small differences in the right barley content order by point method approach. With Partial Least Squares (PLS) regression the amount of barley in Robusta was predicted with high R2 (0.996) and relatively low RMSEP (∼2%) values in case of vision system data processing. Considering electronic tongue measurements, PCA results showed a good discrimination of samples with higher barley concentration. Misclassification was found in the low concentrated area by Lienar Discriminant Analgsis (LDA). To obtain an accurate model for barley content prediction in coffee, the most sensitive sensor signals were used to apply PLS regression successfully (R2=0.97, RMSEP=3.99% (w/w)).

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Postharvest quality changes of two pear and five sweet pepper varieties during cold storage (2±1 °C and 10±1 °C, respectively) and shelf-life (22±2 °C and 20±1 °C, respectively) by non-destructive optical methods (laser backscattering imaging, chlorophyll fluorescence analysis, surface colour measurement) and texture analysis methods (acoustic impulse-response technique, impact method) were determined and monitored. The rate of the change of ‘Conference’ pears’ Fv/Fm chlorophyll fluorescence parameter was lower than for ‘Bosc kobak’, referring to the cultivar characteristic and photosynthetically active chlorophyll content related maturity and colour change. Acoustic and impact stiffness decreased during shelf-life, referring clearly to temperature related textural change. Taking into account the seven different measuring wavelengths (650–1064 nm), laser scattering parameters showed significant and cultivar dependent changes versus time during cold storage and shelf-life. The used non-destructive methods were found to be suitable for objective sweet pepper quality determination. Cold storage combined shelf-life resulted in a relatively longer shelf-life, with a lower intensity and rate of quality decrease in time, based upon mass loss, stiffness, surface colour, and chlorophyll fluorescence changes. ‘Gigant’, ‘Carma’, and ‘Kárpia’ cultivars were found to be favourable, but ‘Kais’ and ‘Kun’ hot pepper samples were really sensitive to quality degradation.

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