Multivariate quality control studies applied to Ca(II) and Mg(II) determination by a portable method

A portable or field test method for simultaneous spectrophotometric determination of calcium and magnesium in water using multivariate partial least squares (PLS) calibration methods is proposed. The method is based on the reaction between the analytes and methylthymol blue at pH 11. The spectral information was used as the X-block, and the Ca(II) and Mg(II) concentrations obtained by a reference technique (ICP-AES) were used as the Y-block. Two series of analyses were performed, with a month's difference between them. The first series was used as the calibration set and the second one as the validation set. Multivariate statistical process control (MSPC) techniques, based on statistics from principal component models, were used to study the features and evolution with time of the spectral signals. Signal standardization was used to correct the deviations between series. Method validation was performed by comparing the predictions of the PLS model with the reference Ca(II) and Mg(II) concentrations determined by ICP-AES using the joint interval test for the slope and intercept of the regression line with errors in both axes. (C) 1998 John Wiley & Sons, Ltd.

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibody in blood sera of domestic cats: quantitative analysis based on partial least-squares multivariate statistics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation via multivariate techniques of scale factor variability in the rietveld method applied to quantitative phase analysis with X ray powder diffraction

ABSTRACT: The present work uses multivariate statistical analysis as a form of establishing the main sources of error in the Quantitative Phase Analysis (QPA) using the Rietveld method. The quantitative determination of crystalline phases using x ray powder diffraction is a complex measurement process whose results are influenced by several factors. Ternary mixtures of Al2O3, MgO and NiO were prepared under controlled conditions and the diffractions were obtained using the Bragg-Brentano geometric arrangement. It was possible to establish four sources of critical variations: the experimental absorption and the scale factor of NiO, which is the phase with the greatest linear absorption coefficient of the ternary mixture; the instrumental characteristics represented by mechanical errors of the goniometer and sample displacement; the other two phases (Al2O3 and MgO); and the temperature and relative humidity of the air in the laboratory. The error sources excessively impair the QPA with the Rietveld method. Therefore it becomes necessary to control them during the measurement procedure.

Why is it so difficult to identify a single indicator of water stress in plants? A proposal for a multivariate analysis to assess emergent properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multivariate analysis for animal selection in experimental research

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multivariate morphometrics and allometry and allometry in a polymorphic ant

In this paper we describe how morphological castes can be distinguished using multivariate statistical methods combined with jackknife estimators of the allometric coefficients. Data from the polymorphic ant, Camponotus rufipes, produced two distinct patterns of allometric variation, and thus two morphological castes. Morphometric analysis distinguished different allometric patterns within the two castes, with overall variability being greater in the major workers. Caste-specific scaling variabilities were associated with the relative importance of first principal component. The static multivariate allometric coefficients for each of 10 measured characters were different between castes, but their relative magnitudes within castes were similar. Multivariate statistical analysis of worker polymorphism in ants is a more complete descriptor of shape variation than, and provides statistical and conceptual advantages over, the standard bivariate techniques commonly used.

Multivariate analysis for selecting animals for experimental research

Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Valutazione della qualità ed autenticità di prodotti oleari e lattiero caseari mediante metodi fisici di analisi accoppiati a tecniche statistiche multivariate

Questa tesi descrive alcuni studi di messa a punto di metodi di analisi fisici accoppiati con tecniche statistiche multivariate per valutare la qualità e l’autenticità di oli vegetali e prodotti caseari. L’applicazione di strumenti fisici permette di abbattere i costi ed i tempi necessari per le analisi classiche ed allo stesso tempo può fornire un insieme diverso di informazioni che possono riguardare tanto la qualità come l’autenticità di prodotti. Per il buon funzionamento di tali metodi è necessaria la costruzione di modelli statistici robusti che utilizzino set di dati correttamente raccolti e rappresentativi del campo di applicazione. In questo lavoro di tesi sono stati analizzati oli vegetali e alcune tipologie di formaggi (in particolare pecorini per due lavori di ricerca e Parmigiano-Reggiano per un altro). Sono stati utilizzati diversi strumenti di analisi (metodi fisici), in particolare la spettroscopia, l’analisi termica differenziale, il naso elettronico, oltre a metodiche separative tradizionali. I dati ottenuti dalle analisi sono stati trattati mediante diverse tecniche statistiche, soprattutto: minimi quadrati parziali; regressione lineare multipla ed analisi discriminante lineare.

Appropriateness of reporting statistical results in orthodontics: the dominance of P values over confidence intervals

The purpose of this study was to search the orthodontic literature and determine the frequency of reporting of confidence intervals (CIs) in orthodontic journals with an impact factor. The six latest issues of the American Journal of Orthodontics and Dentofacial Orthopedics, the European Journal of Orthodontics, and the Angle Orthodontist were hand searched and the reporting of CIs, P values, and implementation of univariate or multivariate statistical analyses were recorded. Additionally, studies were classified according to the type/design as cross-sectional, case-control, cohort, and clinical trials, and according to the subject of the study as growth/genetics, behaviour/psychology, diagnosis/treatment, and biomaterials/biomechanics. The data were analyzed using descriptive statistics followed by univariate examination of statistical associations, logistic regression, and multivariate modelling. CI reporting was very limited and was recorded in only 6 per cent of the included published studies. CI reporting was independent of journal, study area, and design. Studies that used multivariate statistical analyses had a higher probability of reporting CIs compared with those using univariate statistical analyses. Misunderstanding of the use of P values and CIs may have important implications in implementation of research findings in clinical practice.

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Mulch materials in processing tomato: a multivariate approach.

Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L.) crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2), one oxo-biodegradable material (OB), two types of paper (PP1, PP2), and one barley straw cover (BS) were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]). A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

Sea surface and the sea floor regionalization of the Southern Ocean by multivariate cluster analysis, links to ArcGIS project files

This study subdivides the Weddell Sea, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis uses 28 environmental variables for the sea surface, 25 variables for the seabed and 9 variables for the analysis between surface and bottom variables. The data were taken during the years 1983-2013. Some data were interpolated. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared for the identification of the most reasonable method. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested. For the seabed 8 and 12 clusters were identified as reasonable numbers for clustering the Weddell Sea. For the sea surface the numbers 8 and 13 and for the top/bottom analysis 8 and 3 were identified, respectively. Additionally, the results of 20 clusters are presented for the three alternatives offering the first small scale environmental regionalization of the Weddell Sea. Especially the results of 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Benthic macrofauna structure in the northeast area of Todos os Santos Bay, Bahia State, Brazil: patterns of spatial and seasonal distribution

Structure of intertidal and subtidal benthic macrofauna in the northeastern region of Todos os Santos Bay (TSB), northeast Brazil, was investigated during a period of two years. Relationships with environmental parameters were studied through uni-and multivariate statistical analyses, and the main distributional patterns shown to be especially related to sediment type and content of organic fractions (Carbon, Nitrogen, Phosphorus), on both temporal and spatial scales. Polychaete annelids accounted for more than 70% of the total fauna and showed low densities, species richness and diversity, except for the area situated on the reef banks. These banks constitute a peculiar environment in relation to the rest of the region by having coarse sediments poor in organic matter and rich in biodetritic carbonates besides an abundant and diverse fauna. The intertidal region and the shallower area nearer to the oil refinery RLAM, with sediments composed mainly of fine sand, seem to constitute an unstable system with few highly dominant species, such as Armandia polyophthalma and Laeonereis acuta. In the other regions of TSB, where muddy bottoms predominated, densities and diversity were low, especially in the stations near the refinery. Here the lowest values of the biological indicators occurred together with the highest organic compound content. In addition, the nearest sites (stations 4 and 7) were sometimes azoic. The adjacent Caboto, considered as a control area at first, presented low density but intermediate values of species diversity, which indicates a less disturbed environment in relation to the pelitic infralittoral in front of the refinery. The results of the ordination analyses evidenced five homogeneous groups of stations (intertidal; reef banks; pelitic infralittoral; mixed sediments; Caboto) with different specific patterns, a fact which seems to be mainly related to granulometry and chemical sediment characteristics.

Phenolic composition of South American red wines classified according to their antioxidant activity, retail price and sensory quality

In this study, 73 South American red wines (Vitis vinifera) from 5 varietals were classified based on sensory quality, retail price and antioxidant activity and characterised in relation to their phenolic composition. ORAC and DPPH assays were assessed to determine the antioxidant activity, and sensory analysis was conducted by seven professional tasters using the Wine Spirits Education Trust`s structured scales. The use of multivariate statistical techniques allowed the identification of wines with the best combination of sensory characteristics, price and antioxidant activity. The most favourable varieties were Malbec, Cabernet Sauvignon, and Syrah produced in Chile and Argentina. Conversely, Pinot Noir wines displayed the lowest sensory characteristics and antioxidant activity. These results suggest that the volatile compounds may be the main substances responsible for differentiating red wines on the basis of sensory evaluation. (C) 2011 Elsevier Ltd. All rights reserved.