Characterization of olive germplasm by chemical oil components and morphological descriptors in Basilicata region (Italy)

2012 ◽  
Vol 10 (2) ◽  
pp. 145-151 ◽  
Author(s):  
V. Alba ◽  
V. Bisignano ◽  
A. Rotundo ◽  
G. B. Polignano ◽  
E. Alba
Keyword(s):  
A Priori ◽  
Principal Component ◽  
Oil Composition ◽  
Cultivar Group ◽  
Olive Cultivars ◽  
Superior Genotypes ◽  
Coefficients Of Variations ◽  
Oil Components

In this paper, we describe variations among autochthonous olive cultivars from five different areas in Basilicata (Southern Italy) classified according to 33 chemical oil components and morphological traits. While all examined descriptors show no significant differences among cultivars, means and coefficients of variations have been highlighted. Principal component analysis has then been used to reduce the number of descriptors. Cultivars have been classified by cluster analysis into three groups. Following a discussion of cultivar group similarities, results suggest that an ‘a priori’ classification of cultivars according to growing area does not strictly correspond to phenotypic grouping. From the spatial distribution of cultivars, however, it has been possible to identify ‘superior’ genotypes in terms of olive oil composition.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

scholarly journals Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)

Foods ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 233 ◽  
Author(s):  
Olga Escuredo ◽  
María Shantal Rodríguez-Flores ◽  
Sergio Rojo-Martínez ◽  
María Carmen Seijo
Keyword(s):  
Physicochemical Properties ◽  
Principal Component ◽  
Total Content ◽  
Physicochemical Characteristics ◽  
Dependent Variables ◽  
Close Relationship ◽  
Forward Stepwise ◽  
Unifloral Honey

Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showed a close relationship between the physicochemical variables and the botanical origin. The five types of honey presented different physicochemical properties among them. A principal component analysis showed that Hue, lightness, b*, and Chroma variables were important for the honey types classification, followed by Erica pollen, pH, Cytisus, and Castanea variables. A forward stepwise regression analysis was performed introducing as dependent variables the color (mm Pfund) and the Chroma and the Hue variables. The regression models obtained explained 86%, 74%, and 86% of the variance of the data, respectively. The combination of the chromatic and physicochemical and pollen variables through the use of multivariable methods was optimal to characterize and group the honey samples studied.

scholarly journals Agronomic evaluation of cowpea cultivars developed for the West African Savannas

2017 ◽  
Author(s):  
A. Y. Kamara ◽  
S. Ewansiha ◽  
H. Ajeigbe ◽  
L. Omoigui ◽  
A. I. Tofa ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Selection Index ◽  
Principal Component ◽  
Cowpea Cultivars ◽  
Breeding Populations ◽  
Fodder Yield ◽  
Superior Genotypes ◽  
And Cluster Analysis ◽  
On Farm

The goal of this research was to evaluate diverse cowpea genotypes developed over the past 4 decades in the Nigerian Sudan Savannas for their agronomic performance and to identify groups of cultivars with similar quantitative characters. Characterization would facilitate the efficient synthesis of breeding populations for further improvement of cowpea. Also superior genotypes with desirable characteristics could be identified and disseminated in the dry savannas of West Africa. Significant variations were observed in the agronomic characteristics of the cultivars in this study. Principal component analysis (PCA) and cluster analysis were performed on these genotypes and found that there were significant correlations among the variables measured. Modern cultivars outperformed the older ones and from the results of PCA, it was found that the most important variables for the classification of cowpea cultivars are high canopy, high seed weight, high total dry matter, high HI, and high grain and fodder yield. This suggests that these traits could be used in selection index for genetic improvement of cowpea. Cluster analysis resulted in 5 groups mostly corresponding to era of release except cluster I which contained cowpea cultivars from all eras. Two distinct groups in clusters IV and V were identified. Cultivars in cluster IV which were released in the 1990-2000 eras, had high grain and fodder yield. These cultivars could be evaluated on-farm for eventual release to farmers. They could also be used in breeding program for improvements in grain and fodder yield of cowpea. Cluster V contained two cultivars that distinctly had the highest fodder yield suggesting that they could be used to improve fodder yield of cowpea.

scholarly journals Classification of thermal waters based on their inorganic fingerprint and hydrogeothermal modelling

2011 ◽  
Vol 8 (3) ◽  
pp. 4559-4581 ◽  
Author(s):  
I. Delgado-Outeiriño ◽  
P. Araujo-Nespereira ◽  
J. A. Cid-Fernández ◽  
J. C. Mejuto ◽  
E. Martínez-Carballo ◽  
...  
Keyword(s):  
Surface Waters ◽  
Thermal Water ◽  
Principal Component ◽  
Thermal Waters ◽  
Rock Interaction ◽  
Hydrogeochemical Modelling ◽  
Ancient Times ◽  
Water Rock Interaction

Abstract. Hydrothermal features in Galicia have been used since ancient times for therapeutic purposes. A characterization of these thermal waters was carried out in order to understand their behaviour based on inorganic pattern and water-rock interaction mechanisms. In this way 15 thermal water samples were collected in the same hydrographical system. The results of the hydrogeochemistry analysis showed one main water family of bicarbonate type sodium waters, typical in the post-orogenic basins of Galicia. Principal component analysis (PCA) and partial lest squared (PLS) clustered the selected thermal waters in two groups, regarding to their chemical composition. This classification agreed with the results obtained by the use of geothermometers and the hydrogeochemical modelling. The first included thermal samples that could be in contact with surface waters and therefore, their residence time in the reservoir and their water-rock interaction would be less important than for the thermal waters of the second group.

scholarly journals A systems approach to the characterization and classification of T-cell responses

2017 ◽  
Author(s):  
Shinobu Yamamoto ◽  
Elizabeth Whalen ◽  
Daisuke Chujo ◽  
Durgha Nattamai ◽  
Nicole Baldwin ◽  
...  
Keyword(s):  
T Cell ◽  
Systems Approach ◽  
Meta Analysis ◽  
A Priori ◽  
T Cell Responses ◽  
Cell Responses ◽  
Wide Range

AbstractTypes of T-cell responses are categorized on the basis of a limited number of molecular markers selected usinga prioriknowledge about T-cell immunobiology. We sought to develop a novel systems-based approach for the creation of an unbiased framework enabling assessment of antigenic-peptide specific T-cell responsesin vitro. A meta-analysis of transcriptome data from PBMCs stimulated with a wide range of peptides identified patterns of gene regulation that provided an unbiased classification of types of antigen-specific responses. Further analysis yielded new insight about the molecular processes engaged following antigenic stimulation. This led for instance to the identification of transcription factors not previously studied in the context of T-cell differentiation. Taken together this profiling approach can serve as a basis for the unbiased characterization of antigen-specific responses and as a foundation for the development of novel systems-based immune profiling assays.

scholarly journals Simple Sequence Repeat Identification and Endocarp Characterization of Olive Tree Accessions in a Tunisian Germplasm Collection

HortScience ◽  
2010 ◽  
Vol 45 (10) ◽  
pp. 1429-1436 ◽  
Author(s):  
Mahdi Fendri ◽  
Isabel Trujillo ◽  
Ahmed Trigui ◽  
María Isabel Rodríguez-García ◽  
Juan de Dios Alché Ramírez
Keyword(s):  
Simple Sequence Repeat ◽  
Genetic Relationships ◽  
Germplasm Collection ◽  
Olive Tree ◽  
Sequence Repeat ◽  
Olive Cultivars ◽  
Correspondence Factor Analysis ◽  
Simple Sequence

Most traditional olive-producing countries possess a diversified genetic patrimony in Olea europaea L. Since the emergence of modern olive growing system, the identification, classification, and conservation of autochthonous olive cultivars is a priority for these countries. In this work, a total of 84 accessions belonging to the “Boughrara”-Sfax olive germplasm collection located in Tunisia have been screened using a powerful set of eight simple sequence repeat markers (SSRs). The study revealed a high genetic variability among the collection and detected a total of 64 alleles. For better management of the mentioned germplasm bank, an improved classification of the entries, including new denominations, has been proposed. In addition, several cases of mislabeling, synonymy, and homonymy have been clarified. Genetic relationships among cultivars have been analyzed showing four major clusters. Finally, a correspondence factor analysis demonstrated that cultivars tend to cluster depending on their main use as oil or table olives. No clear clustering tendencies were observed when the geographical origin of cultivars was used as the criteria for the analysis. All results obtained by SSR screening and classification were in accordance with classification based on morphological traits of fruit endocarps.

scholarly journals Soil spectral library and its use in soil classification

2010 ◽  
Vol 34 (3) ◽  
pp. 861-870 ◽  
Author(s):  
Henrique Bellinaso ◽  
José Alexandre Melo Demattê ◽  
Suzana Araújo Romeiro
Keyword(s):  
Spectral Data ◽  
Principal Component ◽  
Soil Classification ◽  
Convex Curve ◽  
Spectral Library ◽  
Spectral Curves ◽  
Sensing Applications ◽  
Agricultural Regions

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

scholarly journals Morphologic characterization of spiked pepper's germplasm

2011 ◽  
Vol 29 (2) ◽  
pp. 162-167 ◽  
Author(s):  
José Maria D Gaia ◽  
Milton Guilherme da C Mota ◽  
Carmen Célia C da Conceição ◽  
José Guilherme S Maia
Keyword(s):  
Principal Component ◽  
Molecular Data ◽  
Scatter Diagram ◽  
Breeding Programs ◽  
Multivariate Statistic ◽  
Number Of Leaves ◽  
Superior Genotypes ◽  
Diagram Analysis ◽  
Essential Oil Yield

Spiked pepper is a plant species with properties that allow the development of natural agrochemicals and medicines, showing large potential of use by humanity. With aim to ascertain the phenotypical variability, 41 parentals were analyzed, sampled in the States of Pará and Amazonas. Principal Component analysis and Jolliffe's criterion were utilized for discarding of variables, subsidized by the Pearson's Correlation. It took seven components to explain 80% of the variation. The essential oil yield and number of leaves per branch were suggested to be discarded because they are the characteristics that have contributed least to the total variance. The 3D scatter diagram constituted a relatively homogeneous and continuous clustering, identifing a divergent pair: PA-020 (Marabá-PA) and PA-035 (Santa Isabel-PA). The analyzed traits have variability potentially able to discriminate the parentals, whereas 83.3% of such traits can be used for this purpose. The divergent genotypes identified on 3D scatter diagram analysis can be used in breeding programs for the development of superior genotypes. A comparison with preexisting molecular data of some genotypes permited to conclude that there was one certain agreement degree between morphological and molecular characterizations and that molecular characterization presented higher discriminatory power, using a smaller number of genotypes, identifying dissimilar genotypes and clusters, although analyzed by different multivariate statistic methods.

scholarly journals Psychometric properties of the Impulsive/Premeditated Aggression Scale in Portuguese community and forensic samples

2019 ◽  
Vol 41 (2) ◽  
pp. 144-148 ◽  
Author(s):  
Ana Rita Cruz ◽  
Rita Pasion ◽  
Andreia Castro Rodrigues ◽  
Carmen Zabala ◽  
Jorge Ricarte ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Aggressive Behavior ◽  
Psychometric Properties ◽  
Community Sample ◽  
Principal Component ◽  
Clinical Settings ◽  
European Portuguese ◽  
Forensic Samples

Abstract Introduction Aggression can be defined according to impulsive or premeditated features. Impulsivity is defined as an uncontrolled and unplanned form of aggression. On the contrary, premeditation requires planning and is goal-oriented. Objective The purpose of this study was to validate the basic psychometric properties of the Impulsive/Premeditated Aggression Scale (IPAS) into European Portuguese. The scale evaluates aggression according to impulsive and premeditated features, which are considered the predominant forms of aggressive behavior, and can be used in community, forensic and clinical settings. Methods Participants from a community sample (n = 957; 424 male) and incarcerated individuals (n = 115, all male) completed the IPAS. Results Internal consistency and reliability indicated that the scale has good psychometric properties in both samples. Data from a principal component analysis (PCA) demonstrated similarities to previous structures reported in the literature. Conclusions The scale demonstrated to be sensitive to the bimodal classification of aggression in community and forensic samples, indicating its utility in the characterization of aggressive patterns.

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

2018 ◽  
Vol 72 (12) ◽  
pp. 1774-1780 ◽  
Author(s):  
Irene Marivel Nolasco Perez ◽  
Amanda Teixeira Badaró ◽  
Sylvio Barbon ◽  
Ana Paula AC Barbon ◽  
Marise Aparecida Rodrigues Pollonio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Processing Industry ◽  
Chemical Attributes ◽  
Physical And Chemical

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

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