Search results for "Discriminant Analysis"
showing 10 items of 229 documents
Varietal and geographic classification of french red wines in terms of pigments and flavonoid compounds
1988
Thirty-four French red wines originating from six different grape varieties and three different production areas were analysed in duplicate for 15 anthocyanins, ten flavonoids and three colour parameters, F-statistics, principal component analysis and stepwise discriminant analysis were used to identify and to explain differences among samples. Clear difference between wines made from different varieties were mainly related to anthocyanin 3-acylglucosides. Malvidin and peonidin 3-acetylglucosides were found in increasing concentrations in wines made respectively from Grenache, Carignan, Cinsault, Merlot, Carbernet Sauvignon and Cabernet Franc grapes; the concentrations of peonidin and malvi…
Chemical Element Levels as a Methodological Tool in Forensic Science
2014
The aim of the present study was to define a methodological strategy for understanding how post- mortem degradation in bones caused by the environment affects different skeletal parts and for selecting better preserved bone samples, employing rare earth elements (REEs) analysis and multivariate statistics. To test our methodological proposal the samples selected belong to adult and young individuals and were obtained from the Late Roman Necropolis of c/Virgen de la Misericordia located in Valencia city centre (Comunidad Valenciana, Spain). Therefore, a method for the determination of major elements, trace elements and REEs in bone remains has been developed employing Inductively-Coupled Pla…
Genetic diversity and trait genomic prediction in a pea diversity panel
2014
Background Pea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection. Resu…
Ligophorus pilengas n. sp. (monogenea: ancyrocephalidae) from the introduced so-iuy mullet, mugil soiuy (teleostei: mugilidae), in the sea of Azov an…
2004
The monogenean Ligophorus chabaudi was originally described on the gills of the flathead mullet, Mugil cephalus, and was subsequently reported on the So-iuy mullet, Mugil soiuy. However, the morphology of sclerotized parts and multivariate statistical analyses suggest that the form from the So-iuy mullet represents a new species. This study provides a description of the new species Ligophorus pilengas n. sp. and provides additional morphological data concerning the morphology of the ventral bar that might be useful for the diagnosis of Ligophorus. Ligophorus pilengas n. sp. is the second species of Ligophorus reported on the So-iuy mullet. Zoogeographical records indicate that L. pilengas n…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…
SIMULATION EXPERIMENTS WITH MULTIPLE GROUP LINEAR AND QUADRATIC DISCRIMINANT ANALYSIS
1973
Summary A simulation program is described which can be performed to obtain estimates of the different types of misclassification probabilities for multiple group linear and quadratic discriminant analysis. The program can be used to study how these errors depend on sample sizes and the different parameters of the multivariate normal distribution. Examples for several simulation experiments are given and possible conclusions are discussed.
Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys
2021
Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investi…
ChemInform Abstract: Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds.
2010
In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.
Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds
1998
In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.
Combining near-infrared illuminants to optimize venous imaging
2007
The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. We previously presented work for localizing near-surface veins via near-infrared (NIR) imaging in combination with structured light ranging for surface mapping and robotic guidance. In this paper, we describe experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface. For illumination, we empl…