Application Of Multivariate Methods Research Articles

Application of multivariate methods and hydrochemical model to evaluate industrial mine water discharges from the phosphate beneficiation process, Eshidiya mine, southeast Jordan

Application of multivariate methods and hydrochemical model to evaluate industrial mine water discharges from the phosphate beneficiation process, Eshidiya mine, southeast Jordan

Application of Multivariate Methods in the Investigation of Literary Style: The Phantom of the Opera

Application of Multivariate Methods in the Investigation of Literary Style: The Phantom of the Opera

Application of multivariate methods and geoestatistics to model the relationship between CO2 emissions and physicochemical variables in the Hidrosogamoso reservoir, Colombia

Abstract: Aim This article deals with the estimation of a model for CO2 emissions in the Hidrosogamoso reservoir based on the organic matter level and water quality. This is in order to determine the impact of the creation of a tropical reservoir on the generation of greenhouse gases (GHG), and to establish the water quality and emissions dynamics. We hypothesize that the spatial variability of emissions is determined by water quality and carbon cycling in water. Methods Multivariate techniques were applied to determine the relationships between CO2 and certain physicochemical variables measured in the reservoir between February and May 2015, taking samples in 10 stations and measuring 14 variables (water quality parameters and CO2). Factor, cluster, discriminant and regression analysis, as well as the geostatistical technique kriging, were used. Results We observed that all variables except dissolved organic carbon have strong linear relationships. Nitrate, total-P, total solids and total suspended solids are related due to the presence of nutrients in the water; chlorophyll a and biodegradable dissolved organic carbon due to organic carbon; and alkalinity and dissolved solids due to dissolved minerals. The sampling stations can be classified into two homogeneous groups. The first consists of the stations peripheral to the reservoir and the second of stations inside the reservoir. This difference is due mainly to the behavior of chlorophyll a and biodegradable dissolved organic carbon, and these two variables are also the best predictors for CO2, with a maximum adjustment of 70%. Conclusions Our main conclusion is that the production of CO2 is due to decomposition of flooded organic carbon, depends on the soils flooded and the tributary water quality, and that the production of this gas will, based on the literature, continue for 5 to 10 years depending on the nature of the forest flooded.

Application of multivariate methods in the effectiveness of solvents in the pharmaceutical industry

Los disolventes son agentes quimicos usa-dos en la industria farmaceutica y su importancia radica en que su presencia puede acelerar o retardar la reaccion de un farmaco hasta un millon de veces.Objetivo− La presente investigacion analiza los diferentes tipos de disolventes para evaluar la existencia de grupos en los cuales se puedan identificar patrones relacionados con la efectividad de esos disolventes en la produccion de farmacos.Metodologia− El estudio consta de 4 fases: 1) analisis de componentes principales, 2) analisis de cluster, 3) analisis discriminante, y 4) interpretacion de resultados y conclu-siones.Resultados− Se identificaron tres conglomerados categori-zados como supercriticos, microesfericos y biodegradables. La prueba de T de Hotelling arroja un p-valor de 0, poniendo en evidencia la diferencia entre grupos. El discriminante cuadratico arroja una precision del 96% para la clasifica-cion de disolventes.Conclusiones− El analisis multivariante permite modelar la efectividad de los disolventes en la industria farmaceuti-ca, generando una metodologia objetiva de decision para la clasificacion de disolventes segun un enfoque de efectividad

Biostatistics series module 10: Brief overview of multivariate methods

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after “controlling” for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

(Invited) Applications of Multivariate Methods in the Study of Ionomer Properties

Multivariate data analysis methods are being applied in conjunction with vibrational spectroscopic measurements to gain insights into structure and physical properties of ionomer materials. Infrared and confocal Raman spectroscopy techniques are being adapted to investigate (i) fluorinated ionomer standards that span a range of equivalent weight and (ii) membrane samples with varying tensile properties. Partial least squares (PLS) modeling applied to mid-infrared spectral data sets measured using the attenuated total reflection (ATR) technique is robust in predicting ionomer equivalent weight from measurements on calibration samples. The experimental approach, statistical uncertainties and extension to confocal Raman experiments will be discussed. In relation to membrane structural and tensile properties, vibrational spectra that follow transitions between dispersion, gel and solid membrane ionomer forms are being scrutinized. Least-squares modeling and principal component analysis (PCA) used in combination enhances the ability to resolve spectral features associated with molecular components of the sample that evolve during processing (Figure 1) and to quantitatively track changes in composition over time. Figure 1 shows results from confocal Raman tracking of aqueous Nafion ionomer dispersion through a gel phase. PCA and least-squares modeling were applied to data within the shaded region to extract the indicated spectral components. For membrane processed under different thermal treatments, PLS regression is being applied to examine the correlation between tensile strength and molecular scale structure probed by vibrational spectra. Figure 1

Detecting the addition of sugar and water to wine

Background and Aims Wine fraud has been practised since ancient times, continuing up to the present day and taking many forms. In this study, detection of the addition of unpermitted sugar and water to wines was evaluated using a univariate method, officially adopted, and several more effective multivariate methods. Methods and Results The results are based on 5220 Italian wine samples belonging to the European Union (EU) Wine Databank collected in the period 2000–2010. This databank contains stable isotope ratios for wine samples from many regions over many years. The addition of variable amounts of sugar and water was simulated according to experimental results, and univariate and multivariate methods were compared. It was shown that this type of fraud can be discovered much more easily by the application of multivariate methods. Conclusions It can be difficult to detect the addition of unpermitted sugar and water to wines. With multivariate tests based on the EU Wine Databank, the likelihood of detecting this kind of fraud is significantly increased. Significance of the Study This research evaluates and proposes a more efficient method for statistical interpretation of the EU Wine Databank for evaluation of wine authenticity.

Application of multivariate methods in the monitoring of marine environment: simultaneous determination of bromide, bicarbonate, nitrate and sulphide in seawater by ultraviolet spectroscopy

In this paper we present a new ultraviolet (UV) spectroscopic method for the simultaneous and reagentless determination of bromide, bicarbonate, nitrate and sulphide ions in artificial seawater solutions, applicable to the monitoring of the marine environment. We collected the UV spectra of artificial seawater samples with salinity between 8 and 45 psu, having known concentrations of these anions. As their UV spectra result strongly overlapped, the anion concentrations were determined using the chemometric methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLS) and Multivariate Curve Resolution methods based on Independent Component Analysis (ICA) for the instrumental calibration. These chemometric tools, which are the novelty of the proposed method, allow to save time and cost of analysis. The analytical accuracy attainable by PCR, PLS and ICA based methods were compared and critically discussed, showing the lowest analytical accuracy of PCR with respect to PLS and ICA based methods.

Geochemical discrimination of basaltic sources as a tool for provenance analyses of bifacial tools in the southern Levant: first results from the Jezreel Valley, Israel

The determination of groundstone tool sources bears the potential to examine aspects like raw material selection and preference, mobility, trade and exchange patterns, control over resources and long term use of raw material sources. The discovery of the Neolithic/Chalcolithic basanite bifacial quarry of Giv'at Kipod in the Jezreel Valley, Israel, provides the opportunity of raw material centred provenance studies of bifacials in the southern Levant. The basis for reliable provenance analyses is a clear geochemical characterization of the extraction site and its discrimination from other potential sources. To achieve this, the Miocene magmatic rocks of the Jezreel Valley were sampled and analysed by XRF and La-ICP-MS. The geochemical evaluation, combined with cluster analyses, resulted in a clear discrimination of the Giv'at Kipod lava from other basaltic sources in the area. Based on the geochemical field data, a Giv'at Kipod provenance for six bifacial tools found in three archaeological sites dated to the Neolithic and the Chalcolithic periods was established. The results suggest that the quarry was in use for several millennia. This pilot study demonstrates that for provenance analyses lava outcrops in the southern Levant can be geochemically discriminated from each other on a very small spatial scale, on the basis of a detailed field sampling and the application of multivariate methods.

High Speed Data Processing for Imaging MS-Based Molecular Histology Using Graphical Processing Units

Imaging MS enables the distributions of hundreds of biomolecular ions to be determined directly from tissue samples. The application of multivariate methods, to identify pixels possessing correlated MS profiles, is referred to as molecular histology as tissues can be annotated on the basis of the MS profiles. The application of imaging MS-based molecular histology to larger tissue series, for clinical applications, requires significantly increased computational capacity in order to efficiently analyze the very large, highly dimensional datasets. Such datasets are highly suited to processing using graphical processor units, a very cost-effective solution for high speed processing. Here we demonstrate up to 13× speed improvements for imaging MS-based molecular histology using off-the-shelf components, and demonstrate equivalence with CPU based calculations. It is then discussed how imaging MS investigations may be designed to fully exploit the high speed of graphical processor units.

Preamble for special issue honouring John F. MacGregor

Preamble for special issue honouring John F. MacGregor

Multivariate methods in enterprise system implementation, risk management and change management

This work is a first step towards the application of multivariate methods in risk management and change management in Enterprise System Implementation (ESI). ESI is characterised by concentrated efforts to integrate an IT system. Such projects typically experience unplanned problems and events, which may lead to major restructuring of the process. In this work, we rely on ESI theory developed by the Better Enterprise SysTem (BEST) project and more conventional risk management methodology. Both change management and risk management consist of data collection and data analysis. We will show that the data structures of both efforts are similar so that similar data analysis techniques are applicable. In fact one can consider change management as a special case of risk management. This paper focuses on the application of multivariate methods in comparing risk profiles and readiness assessments at various stages of an ESI project. The techniques are correspondence analysis and partial order mapping, which help to characterise and compare ESI readiness across different parts of a company and compare risk profiles of different ESI components.

Phytoplankton as an Indicator of the Water Quality of the Deep Lakes South of the Alps

This paper offers a synoptic account of studies on the phytoplankton communities in the deep southern subalpine lakes (DSL) Garda, Iseo, Como, Lugano and Maggiore. The main cause of the degradation of the water quality in the DSL is eutrophication. The euphotic layers of these lakes are trophically different, ranging from the oligo-mesotrophy of lakes Maggiore and Garda to the meso-eutrophy of lakes Iseo and Lugano. The trophic status as estimated by using total phosphorus and chlorophyll a has provided consistent results in agreement with the models proposed by OECD (1982. Eutrophication of Waters. Monitoring, Assessment and Control, OECD, Paris). Though related with chlorophyll a and TP, the Secchi disk depths have significantly underestimated the trophic status of the DSL. Two trophic indices using the algal orders (PTIorders) and species (PTIspecies) were drawn up on the basis of the distribution of phytoplankton along a trophic gradient defined by the application of multivariate methods; the scores emerging from these indices were used to make a definitive ecological classification of water bodies on a scale from 1 to 5, in accordance with the Water Framework Directive. A third index (PTIOE) was computed as the ratio between the annual mean values of the cumulative biovolumes of two groups of algal orders with opposite trophic characteristics. The three PTI indices were highly correlated, providing a consistent classification of the water bodies. The indices proposed in this work were specifically adopted for use in the DSL. However, the criteria for their implementation constitute a robust and impartial tool for assessing similar indices in other lake typologies and for evaluating the degree of specificity of the trophic indicator values assigned to the single phytoplankton orders and species.

Application of multivariate methods to identify the indices of secondary proteolysis for Trappist cheese maturity and quality

Secondary proteolytic data (size-exclusion chromatography and spectrophotometric values) were utilised to characterise the degree of ripening of Hungarian Trappist cheese. The proteolytic parameters were determined during the ripening period and shelf life in a search for a correlation between the changes in the proteolytic parameters and the age or sensory properties of the product. The investigated Trappist cheese samples, originating from five different manufacturing processes, were graded by means of chemical composition and sensory tests. The proteolytic data were evaluated with statistical methods (single-valued and multivariate analysis). Some proteolytic parameters were found to be useful as ripening indices to characterise the maturity of Hungarian Trappist cheese.

Multivariate evaluation of the fluorescence of aquatic organic matter

Analysis of three-dimensional fluorescence spectra has proved to be a valuable tool in the characterisation of fluorescent dissolved organic matter (FDOM). Application of multivariate methods is a development that further enhances the capabilities of fluorescence spectroscopy for qualitative as well as quantitative analysis. In this work, principal component analysis (PCA) and partial least squares regression (PLS) are applied on fully corrected excitation–emission matrices (EEMs). The relation between sampling site and fluorescence properties in a coastal environment is analysed with PCA and PLS. The applicability of fluorescence spectroscopy and multivariate analysis for the evaluation of properties of humic substances (HS) is discussed.

An Ordination Study of Montane Grasslands of Sri Lanka

(1) Ordinations were made by reciprocal averaging of higher plant and soil microfungal data from montane grasslands (patanas) of Sri Lanka. (2) Ordinations of stands based on both the higher plants and soil fungi reaffirmed the previously-postulated ecological distinctiveness of five major zones within the patanas. Supporting evidence was provided by the ecological affinities of species-groups produced by ordinations of the higher plants and soil fungi. (3) Applications of multivariate methods to higher plant and fungal data from the same ecosystem are regarded as alternative approaches providing complementary ecological information, and it is shown that a combination of the two approaches is more informative than either alone.

A Comparison of Some Cotswold Beechwoods Using Multiple-Discriminant Analysis

In many plant ecological studies information is available about the performance of species at a number of sites, either in the form of simple 0/1 data or with additional measures of cover/abundance or frequency. The applicability of multivariate methods to the study of such data has been shown by the many uses of ordination and classification in the recent literature. The general aim is to discover grouping or an underlying structure within the data, which is considered as a single multivariate statistical universe. A different problem arises when there are several multivariate statistical universes which have already been defined as distinct by the investigator on some external criterion; the internal structure of any of these, separately or all together, may have been examined previously. The concern now, however, is how these groups may be compared and hypotheses generated about the reasons for their differences. A method suitable for this is Multiple-Discriminant Analysis (MDA), a term we prefer to canonical analysis as used by Seal (1964) and others since it is more explicit and avoids possible confusion with the method of canonical correlation. We know of no report on the use of MDA in the plant ecological literature, but it has been used by Buzas (1967) to compare faunal areas, Healy (1965) to compare groups of mice, Horton, Russell & Moore (1968) to examine differences between soil types in a gilgaied area, and others. This paper reports the application of MDA to ground flora data from some Cotswold beechwoods. There are thirteen groups, each corresponding to a woodland area defined as distinct on the ground. In the initial stages of this work the data of the thirteen woods were considered together and analysed by Association-Analysis and Principal Components Analysis. From these results (Barkham 1968) it appeared that the woodland might be of interest as an ecological unit and within-wood variation was considered first. Thus in investigating the between-wood variation in the present report the results of the previous series of analyses have been taken into account. The differences between woods may be due to differences inherent in varying physical site conditions. But if the latter are similar for two or more woods then the differences in vegetation may be accounted for by variation in management practices. Whichever group of factors may be found most significant it is important to assess differences between woods because the wood is the most frequent unit in forestry and conservation management. In each of the w woods the presence and cover, or absence, of the same species is recorded at a number of sites. Each wood can be represented by its vector of species means (on the measure chosen, see below) and located in m-dimensional space. The discriminant functions, or axes, are a new set of orthogonal axes in this fixed space positioned so that the first accounts for as much as possible of the difference between the wood* Present address: School of Environmental Sciences, University of East Anglia.

Guest Editorial: Toward Broader Applications of Multivariate Methods.

Guest Editorial: Toward Broader Applications of Multivariate Methods.

Identifying the “grey” areas—A multivariate approach

Smith D. M. (1968) Identifying the “grey” areas—A multivariate approach, Reg. Studies 2, 183–193. The setting up of the Hunt Committee to look into problems of the “grey” areas raises the question of how to identify areas of economic distress. Up to now the sole criterion in Britain has been unemployment. But unemployment correlates weakly with many other possible measures of economic health. The application of multivariate methods and related grouping procedures to twenty-eight indicators of industrial and socio-economic health in North West England provides the basis for a more comprehensive definition of the distressed areas. The results show that the degree of distress may be considerably greater outside the Development Areas than within them.

Some Applications of Multivariate Methods in Botany

Some Applications of Multivariate Methods in Botany