At present the grasslands in the conservation areas are often degraded folowing the abandonment of the pasture and it is necessary to restore them. The aim of this paper was to evaluate the grasslands after the ecological restoration and phytoremediation by different methods. In 2004 the experiment was established on degraded sites at locality Low Tatras National Park (1 126 m a.s.l.) with three treatments (U – unmanaged control, C – managed by cutting, CS – managed by seeding and cutting). In 2013 on C treatment we noticed the increase of the number of species (threefold), the increase of the coverage of plants (twofold) and also the significant increase of the species diversity in comparison with the year 2004. On CS treatment the species diversity has decreased slightly but the forage value has increased more than twofold since 2004. U treatment has remained without changes. The results show the method of seeding the autochthonous species is highly appropriate to increase the forage value of grassland. Spearman correlations between environmental factors (p ≤ 0.05) prove the effect of treatment on the amount of P-soil, P-fyt, K-soil and K-fyt, which show strong negative correlation with the time. On the other hand number of species and EGQ (The evaluation of the grassland quality)correlate with time positively (p ≤ 0.05). The restoration by the mowing is recommended on sites, where the increase of diversity is important. Legumes, C, grasses, K-soil, EGQ, number of species, P-soil, time, dry matter hit the variability of the species significantly (to 96.80% of the total variability). The strongest effect on the overall variability of species had legumes, which is 61% of the total variability. Cutting explain 26% of the variability. The soil in the Low Tatras National Park was recovered to the original state through the reduction of soil nutrient – phytoremediation.

The Danish food system has undergone a transition in the past 10-20 years, in which new quality conventions have evolved. Examples include, increasing organic production and consumption, increasing interest in local food, experience, community, taste and gastronomy. This article explores the barriers and opportunities for enabling new modes of production in the food system. We conduct a multiple comparative case study involving three product categories (craft beer, specialty flour and organic broilers). The cases are described using, statistical data, document analysis and individual interviews. Craft beer and specialty flour are characterized by a revolution of conventions, in which new flavors, new products, new practices and new social relations are generated; by contrast, organic broilers have not fulfilled its market potential. The case studies demonstrate that the revolution is not just taking place in one domain, but it implies a multidimensional reconfiguration of the food system and involve a complex transformation of material practices, institutions, social networks and cultural frames. In addition, the cases of craft beer and specialty flour demonstrate that a diversification of product categories, engaging consumers and the appreciation of aesthetic qualities are a critical factor in the transition.

The Danish food system has undergone a transition in the past 10-20 years, in which new quality conventions have evolved. Examples include, increasing organic production and consumption, increasing interest in local food, experience, community, taste and gastronomy. This article explores the barriers and opportunities for enabling new modes of production in the food system. We conduct a multiple comparative case study involving three product categories (craft beer, specialty flour and organic broilers). The cases are described using, statistical data, document analysis and individual interviews. Craft beer and specialty flour are characterized by a revolution of conventions, in which new flavors, new products, new practices and new social relations are generated; by contrast, organic broilers have not fulfilled its market potential. The case studies demonstrate that the revolution is not just taking place in one domain, but it implies a multidimensional reconfiguration of the food system and involve a complex transformation of material practices, institutions, social networks and cultural frames. In addition, the cases of craft beer and specialty flour demonstrate that a diversification of product categories, engaging consumers and the appreciation of aesthetic qualities are a critical factor in the transition.

The quality standards of citrus for fresh consumption are mainly based on the absence of bruises and rotting, as well as an adequate shape, color, and size. Current industrial systems for inspection require the processing of images in a few milliseconds, inspecting the whole surface of the fruit moving on a conveyor belt. Most of them are limited to the visible region of the electromagnetic spectrum, but the identification of defects is an important task that still remains unresolved. Information provided by near infrared or ultraviolet regions of the spectrum may improve the accuracy of fruit identification and the detection of dangerous damages or diseases that evolve or are invisible to our eyes. Furthermore, some techniques open the possibility of inspecting the internal parts of the fruits. This chapter summarizes research related to the application of machine vision, including different imaging technologies and techniques for the automated inspection of the quality of citrus fruits.

Predictive clustering [1] is a new supervised learning framework derived from traditional clustering. These algorithms start by identifying pure clusters (in terms of classes) that have a high probability density. Based on the information given by the clusters, these algorithms can predict the class of new instances. Compared to supervised classification, predictive clustering can discover the internal structure of the target class. It thus allows users to find the different reasons behind the same prediction: two heterogeneous instances could have the same predicted label. By its nature, predictive clustering incorporates the characteristics of both supervised classification and clustering. Thus, in the evaluation of predictive clustering results, three points should be taken into account: a high intra-cluster similarity, a low inter-cluster similarity and a good prediction rate. A predictive clustering quality criterion must balance these three points. In this this work, we propose a new criterion for measuring the predictive clustering quality. This criterion calculates the compactness and the separability of clusters using a new supervised similarity measure. This measure exploits the information given by the target class in such way that two instances are considered similar if and only if a distance between them is small and they belong to the same class. And, they are considered heterogeneous if and only if a distance between them is large and they belong to different classes. The obtained results from different simulated datasets show that the proposed criterion constantly gives the optimal number of clusters. To our knowledge, there is no analytic criterion in the state of the art that is able to measure the quality of the results generated by predictive clustering algorithms (the trade-off mentioned above) and therefore to compare with our suggested criterion. So, to compare our results, we use the well know unsupervised criterion (Davies-Bouldin) [2] and two supervised criteria (Adjusted Rand Index [3] and Variation of Information [4]) and we examine if our criterion find the good tradeoff.

Quality of pre-processed food grains is a critical aspect and a major decider of market acceptability, storage stability, processing quality, and overall consumer acceptance. Among various indices of food grain quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the grain. Conventional method of grain quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy.Machine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for grain quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems.Machine vision systems can provide rapid and accurate information about external quality aspects of food grains. However, it is a task to integrate such systems with those that can explain internal grain quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various grain quality evaluation applications.

Resumen: En Veracruz, México, el cultivo de café (Coffea arabica L.) se hace predominantemente con sistema de producción bajo sombra. Las principales variedades son Typica, Bourbon y Caturra, y la producción promedio de frutos por planta es baja en comparación con la de otros países. Aquí se evaluó la producción de fruta (café cereza), el rendimiento agroindustrial cereza-pergamino y la calidad sensorial de la bebida en 20 cultivares de café durante cinco ciclos de producción (1998 a 2003). Las variedades con mayor producción promedio de fruto (café cereza) a través de esos cinco años fueron: Catuai Amarillo (23.8 kg/planta), Caturra Rojo (22.6 kg/planta), Colombia Brote Café (23.2 kg/planta) y Colombia Brote Verde (22.5 kg/planta). La variedad con mayor rendimiento agroindustrial cereza-pergamino fue Pluma Hidalgo 177 con 237.3 kg. El rendimiento de pergamino-oro fue mejor para Colombia Brote Verde (54.7 kg) en comparación con Garnica Tres Cruces Porte Alto (59.3 kg). La proporción de granos planchuela fue alta para Caturra Amarillo y Blue Mountain, con 86.2 y 83.2 % respectivamente. Pacamara tuvo la menor producción de fruto, pero presentó la mayor proporción de granos para preparación europea; este cultivar también presentó los mejores atributos en aroma, acidez y cuerpo. Para una preparación de café tipo Americano, las variedades Bourbon Salvadoreño y Bourbon Tres Cruces, tuvieron el tamaño de grano adecuado. | Abstract: In Veracruz, México coffee (Coffea arabica L.) plantations are mainly grown under the shaded production system. The varieties typically used are Typica, Bourbon and Caturra, and their average fruit yield per plant is low compared to other countries. Fruit yield (cherry coffee), agro-industrial yield of cherry-parchment coffee, and sensorial quality of the beverage were evaluated in 20 coffee cultivars growing in Veracruz, México, during five years of production (1998 to 2003). Varieties with higher average fruit yield (cherry coffee) during the evalulation period were: Catuai Amarillo (23.8 kg/plant), Caturra Rojo (22.6 kg/plant), Colombia Brote Café (23.2 kg/plant) and Colombia Brote Verde (22.5 kg/plant). The variety with the highest agro-industrial yield cherry-parchment was Pluma Hidalgo 177 with 237.3 kg. Colombia Brote Verde produced more parchment-green coffee (54.7 kg) compared to Garnica Tres Cruces Porte Alto (59.3 kg). The largest proportion of normal grains corresponded to Caturra Amarillo and Blue Mountain, with 86.2 and 83.2 % respectively. Cv. Pacamara had lower fruit yield but presented the highest proportion of grains for preparing European-style coffee; this cultivar also achieved the best attributes in smell, acidity and body. For the preparation of American-style coffee, varieties Bourbon Salvadoreño and Bourbon Tres Cruces achieved adequate grain size.

This study investigated the acceptance of pork with varying levels boar-taint related off-flavours both, within a meat-alone (pure) and a meal context. In total, backfat samples of n=24 animals were evaluated by a trained panel. The fat score was then related to the consumer liking of the pork chops. Repeated ANOVA of chop liking with consumer as a random factor (n=37) and fat score as an interval predictor shows neither a main effect of context (dwithin=0.015) nor the interactions of context with linear and quadratic coefficient of the fat score. The linear (b=−0.20) and quadratic (b=−0.24) coefficients of the fat score main effect demonstrate the necessity and effectiveness of sensory quality control at slaughter. The quadratic coefficient showed a distinct penalty for higher fat scores. Sensory defects detected by trained panellists may not be noticed by usually less sensitive consumers.