<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oliveira, Vanda</style></author><author><style face="normal" font="default" size="100%">Knapic, Sofia</style></author><author><style face="normal" font="default" size="100%">Pereira, Helena</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines</style></title><secondary-title><style face="normal" font="default" size="100%">Food and Bioproducts Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Classification algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">natural cork stoppers</style></keyword><keyword><style  face="normal" font="default" size="100%">Porosity</style></keyword><keyword><style  face="normal" font="default" size="100%">quality classes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Abstract The natural cork stoppers are commercially graded into quality classes according with the homogeneity of the external surface. The underlying criteria for this classification are subjective without quantified criteria and standards defined by cork industry or consumers. Image analysis was applied to premium, good and standard quality classes to characterize the surface of the cork stoppers and stepwise discriminant analysis (SDA) was used to build predictive classification models. The final goal is to analyze the contribution of each porosity feature and propose an algorithm for cork stoppers quality class classification. This study provides the knowledge based on a large sampling to an accurate grading of natural cork stoppers.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferreira-Dias, Suzana</style></author><author><style face="normal" font="default" size="100%">Valente, Dina G.</style></author><author><style face="normal" font="default" size="100%">Abreu, José M.F. F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern recognition of acorns from different Quercus species based on oil content and fatty acid profile</style></title><secondary-title><style face="normal" font="default" size="100%">Grasas y Aceites</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">acorn</style></keyword><keyword><style  face="normal" font="default" size="100%">cluster analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Oil</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Quercus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">384-391</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim of this study was (i) to characterize different species of Quercus genus and (ii) to discriminate among them on the basis of the content and fatty acid composition of the oil in their fruits and/or their morphological aspects via pattern recognition techniques (Principal Component Analysis, PCA, Cluster Analysis, CA, and Discriminant Analysis, DA). Quercus rotundifolia Lam., Quercus suber L. and Quercus pyrenaica Willd., grown in the same stand in the centre of Portugal, were investigated. When oil content and respective fatty acid composition were used to characterize samples, well-separated groups corresponding to each of the species were observed by PCA and confirmed by CA and DA. The ‘‘width’’ and ‘‘length’’ of acorns exhibited a low discriminant power. Acorns from Q. rotundifolia showed the highest average oil content followed by Q. suber and Q. pyrenaica acorns (9.1, 5.2 and 3.8%, respectively). Fatty acid profiles of Q. rotundifolia and Q. suber oils are similar to olive oil while the oil from Q. pyrenaica acorns is more unsaturated</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferreira-Dias, Suzana</style></author><author><style face="normal" font="default" size="100%">Valente, Dina G.</style></author><author><style face="normal" font="default" size="100%">Abreu, José M. F. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern recognition of acorns from different Quercus species based on oil content and fatty acid profile</style></title><secondary-title><style face="normal" font="default" size="100%">Grasas y Aceites</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">acorn</style></keyword><keyword><style  face="normal" font="default" size="100%">cluster analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Oil</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Quercus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://grasasyaceites.revistas.csic.es/index.php/grasasyaceites/article/view/224/224</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">384 - 391</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim of this study was (i) to characterize different species of Quercus genus and (ii) to discriminate among them on the basis of the content and fatty acid composition of the oil in their fruits and/or their morphological aspects via pattern recognition techniques (Principal Component Analysis, PCA, Cluster Analysis, CA, and Discriminant Analysis, DA). Quercus rotundifolia Lam., Quercus suber L. and Quercus pyrenaica Willd., grown in the same stand in the centre of Portugal, were investigated. When oil content and respective fatty acid composition were used to characterize samples, well-separated groups corresponding to each of the species were observed by PCA and confirmed by CA and DA. The ‘‘width’’ and ‘‘length’’ of acorns exhibited a low discriminant power. Acorns from Q. rotundifolia showed the highest average oil content followed by Q. suber and Q. pyrenaica acorns (9.1, 5.2 and 3.8%, respectively). Fatty acid profiles of Q. rotundifolia and Q. suber oils are similar to olive oil while the oil from Q. pyrenaica acorns is more unsaturated</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">González-Paramás, Ana M</style></author><author><style face="normal" font="default" size="100%">García-Villanova, Rafael J</style></author><author><style face="normal" font="default" size="100%">Gómez Bárez, J Alfonso</style></author><author><style face="normal" font="default" size="100%">Sánchez Sánchez, José</style></author><author><style face="normal" font="default" size="100%">Ardanuy Albajar, Ramón</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Botanical origin of monovarietal dark honeys (from heather, holm oak, pyrenean oak and sweet chestnut) based on their chromatic characters and amino acid profiles</style></title><secondary-title><style face="normal" font="default" size="100%">European Food Research and Technology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">amino acids profile</style></keyword><keyword><style  face="normal" font="default" size="100%">analysis of colour</style></keyword><keyword><style  face="normal" font="default" size="100%">botanical discrimination</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">monovarietal honeys</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">226</style></volume><pages><style face="normal" font="default" size="100%">87-92</style></pages><isbn><style face="normal" font="default" size="100%">0021700605</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An early optimisation of the o-phthaldialdehyde (OPA) method for the HPLC-ﬂuorimetric analysis of amino acids in honey and bee-pollen made feasible the quantiﬁ- cation of 23, which included ﬁve infrequently searched for. Forty samples of monovarietal dark honeys, from four botanical origins, were analysed for their amino acids’ proﬁle (which included proline) and their colour. In order to test for their botanical classiﬁcation stepwise linear discriminant analysis was applied to a number of variables made from a selection of analytical results and simple mathematical functions of them. This allowed, ﬁrstly, distinguishing honeys from these four botanical sources with 85.0% cross-validated grouped cases correctly classiﬁed. A second application was made for the group of honeydew honeys—a matter of particular interest since no chemical or microscopic method exists for the honeys from holm oak and oak. Ninety-ﬁve percent of the cross-validated samples were correctly classiﬁed, only one sample was misclassiﬁed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Laurent, J.-M.</style></author><author><style face="normal" font="default" size="100%">Bar-Hen, A</style></author><author><style face="normal" font="default" size="100%">François, L</style></author><author><style face="normal" font="default" size="100%">Ghislain, M</style></author><author><style face="normal" font="default" size="100%">Cheddadi, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Refining vegetation simulation models: From plant functional types to bioclimatic affinity groups of plants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CARAIB</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hierarchical clusteranalysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Moisture</style></keyword><keyword><style  face="normal" font="default" size="100%">pollen</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasonality</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Blackwell Publishing Ltd</style></publisher><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">739-746</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Question: How to refine simulations based on a global vegetation model in order to apply it to regional scale? Location: Europe from 35° N to 71° N and 25° W to 70° E. Methods: Geographical ranges of European plants were georeferenced and used with monthly mean climatic data (diurnal temperature ranges, ground frost frequencies, precipitation, relative humidity, rain frequencies, amount of sunshine hours and temperature) and growing degree days to infer climatic boundaries for 320 taxa. We performed a discriminant analysis to define their potential geographic ranges. Hierarchical clustering was computed on potential ranges. Results: Clustering provided 25 Bioclimatic Affinity Groups (BAG) of plants consisting of 13 tree, seven shrub and five herb groups. These B AGs are characterized by different geographical ranges and climatic tolerances and requirements. Conclusion: The use of monthly data instead of annual values improved the prediction of potential distribution ranges and highlighted the importance of climate seasonality for defining the plant groups with accuracy. The B AGs are detailed enough to provide finer reconstructions and simulations of the vegetation at the regional scale.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Laurent, J.-M.</style></author><author><style face="normal" font="default" size="100%">Bar-Hen, A.</style></author><author><style face="normal" font="default" size="100%">François, L.</style></author><author><style face="normal" font="default" size="100%">Ghislain, M.</style></author><author><style face="normal" font="default" size="100%">Cheddadi, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Refining vegetation simulation models: From plant functional types to bioclimatic affinity groups of plants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CARAIB</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Hierarchical clusteranalysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Moisture</style></keyword><keyword><style  face="normal" font="default" size="100%">pollen</style></keyword><keyword><style  face="normal" font="default" size="100%">Seasonality</style></keyword><keyword><style  face="normal" font="default" size="100%">Temperature</style></keyword><keyword><style  face="normal" font="default" size="100%">Vegetation distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1111/j.1654-1103.2004.tb02316.x</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">739 - 746</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Question: How to refine simulations based on a global vegetation model in order to apply it to regional scale? Location: Europe from 35° N to 71° N and 25° W to 70° E. Methods: Geographical ranges of European plants were georeferenced and used with monthly mean climatic data (diurnal temperature ranges, ground frost frequencies, precipitation, relative humidity, rain frequencies, amount of sunshine hours and temperature) and growing degree days to infer climatic boundaries for 320 taxa. We performed a discriminant analysis to define their potential geographic ranges. Hierarchical clustering was computed on potential ranges. Results: Clustering provided 25 Bioclimatic Affinity Groups (BAG) of plants consisting of 13 tree, seven shrub and five herb groups. These B AGs are characterized by different geographical ranges and climatic tolerances and requirements. Conclusion: The use of monthly data instead of annual values improved the prediction of potential distribution ranges and highlighted the importance of climate seasonality for defining the plant groups with accuracy. The B AGs are detailed enough to provide finer reconstructions and simulations of the vegetation at the regional scale.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: Blackwell Publishing Ltd</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gavilán, Rosario</style></author><author><style face="normal" font="default" size="100%">Fernández-González, Federico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Climatic discrimination of Mediterranean broad-leaved sclerophyllous and deciduous forests in central Spain</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bioclimatic indices</style></keyword><keyword><style  face="normal" font="default" size="100%">climate</style></keyword><keyword><style  face="normal" font="default" size="100%">Continentality</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Summer aridity</style></keyword><keyword><style  face="normal" font="default" size="100%">Water availability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1997///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.2307/3237327</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">377 - 386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Abstract. Climatic differences between three types of deciduous (Quercus pyrenaica) and three types of sclerophyllous (Quercus rotundifolia) Mediterranean forests in the Spanish Sistema Central were analyzed by means of Canonical Discriminant Analysis and Jancey's Discriminant Analysis, applied in successive steps to data from 252 meteorological stations. Climatic data included temperature and precipitation records as well as bioclimatic indices. Discriminant analysis was applied to broad-leaved sclerophyllous and deciduous forest communities sampled at each meteorological station using phytosociological methods. Annual and seasonal (summer, spring) water availability are the most important factor controlling the distribution of the two physiognomic forest types; southwestern associations of Quercus pyrenaica and Q. rotundifolia differ from their colder homologues by annual and monthly temperatures; western associations were separated from eastern ones in terms of annual and seasonal precipitation gradients. Discriminant analysis was a good technique to explore climatic gradients not shown by other general ordination or classification methods.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: Blackwell Publishing Ltd</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gavilán, Rosario</style></author><author><style face="normal" font="default" size="100%">Fernández-González, Federico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Climatic discrimination of Mediterranean broad-leaved sclerophyllous and deciduous forests in central Spain</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bioclimatic indices</style></keyword><keyword><style  face="normal" font="default" size="100%">climate</style></keyword><keyword><style  face="normal" font="default" size="100%">Continentality</style></keyword><keyword><style  face="normal" font="default" size="100%">Discriminant analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Summer aridity</style></keyword><keyword><style  face="normal" font="default" size="100%">Water availability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><publisher><style face="normal" font="default" size="100%">Blackwell Publishing Ltd</style></publisher><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">377-386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Abstract. Climatic differences between three types of deciduous (Quercus pyrenaica) and three types of sclerophyllous (Quercus rotundifolia) Mediterranean forests in the Spanish Sistema Central were analyzed by means of Canonical Discriminant Analysis and Jancey's Discriminant Analysis, applied in successive steps to data from 252 meteorological stations. Climatic data included temperature and precipitation records as well as bioclimatic indices. Discriminant analysis was applied to broad-leaved sclerophyllous and deciduous forest communities sampled at each meteorological station using phytosociological methods. Annual and seasonal (summer, spring) water availability are the most important factor controlling the distribution of the two physiognomic forest types; southwestern associations of Quercus pyrenaica and Q. rotundifolia differ from their colder homologues by annual and monthly temperatures; western associations were separated from eastern ones in terms of annual and seasonal precipitation gradients. Discriminant analysis was a good technique to explore climatic gradients not shown by other general ordination or classification methods.</style></abstract></record></records></xml>